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Normative beliefs and modality styles: a latent class and latent variable model of travel behaviour

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Abstract

We study the interrelation of normative beliefs, which are an individual’s perception of the beliefs of others regarding a specific behaviour, and modality styles, which represent the part of an individual’s lifestyle that is characterised by the use of a certain set of modes. In recent years, travel behaviour research has increasingly sought to understand the effect of social influence on mobility-related behaviour. One stream of literature has adopted representations rooted in social psychology to explain behaviour as a function of latent psycho-social constructs including normative beliefs. Another stream of literature has employed a lifestyle-oriented approach to identify segments or modality styles within a population that differ in terms of their orientation towards different modes of transport. Our study proposes an integrated conceptual framework that combines elements of these two streams of literature. Modality styles are hypothesised to be a function of normative beliefs towards the use of different modes of transport; mobility-related attitudes and behaviours are in turn hypothesised to be functions of modality styles. The conceptual model is operationalised using a latent class and latent variable model and empirically validated using data collected through an Australian consumer panel. We demonstrate how this integrated model framework may be used to understand the relationship between normative beliefs, modality styles and travel behaviour. In addition, we show how the model can be applied to predict how extant modality styles and patterns of travel behaviour may change over time in response to concurrent shifts in normative beliefs.

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Notes

  1. We acknowledge that applications of HCM in travel behaviour analysis have been critiqued. Chorus and Kroesen (2014) argue that the derivation of policy implications, which suggest to change the value of a latent variable to stimulate the demand for more sustainable travel modes, from HCMs, which include attitudes and perceptions as latent explanatory variables, is problematic, because such latent variables are likely to be endogenous to travel behaviour. In essence, Chorus’s and Kroesen’s (2014) critique alludes to the importance of robustifying the specification of HCMs against endogeneity. Yet, we argue that the use of HCMs in policy analysis is not per se problematic, provided that they are specified in a way that is consistent with behavioural theory

  2. Moreover, from the vector \(\hat{\tau }_{b}\) of estimated threshold parameters, the distribution of responses to question \(b \in B\) can be calculated. Let \(\hat{\tau }_{{\varvec{b},\eta_{b} }}\) be the estimated threshold between responses \(\mu_{b,k,\eta }\) and \(\mu_{{b,m,\eta_{b + 1} }}\).Then, \(\frac{{\exp \,(\hat{\tau }_{{\varvec{b},\eta_{b} }} - \rho_{b} )}}{{1 + \exp\, (\hat{\tau }_{{\varvec{b},\eta_{b} }} - \rho_{b} )}}\) is the cumulative probability of observing \(\{ \mu_{b,k,0} , \ldots ,\mu_{{b,k,\eta_{b} }} \}.\)

  3. To mitigate potential response biases due to interviewer demand effects, the items for the measurement of latent constructs, were phrased, using neutral language. Furthermore, the order of the items was randomised across respondents. Similarly, the order of items within blocks of items pertaining to a specific transport mode were randomised, as was the order of the blocks. In addition, each block of items contained a roughly equal number of items, i.e., items pertaining to certain transport modes were neither over- nor under-represented in the questionnaire.

  4. To reduce the response burden, indicators pertaining to the use of the private car were only displayed to respondents, who had indicated that they held a driving license. Similarly, the indicators pertaining to bicycling were only displayed to respondents, who had access to a bicycle on a regular basis.

  5. The survey instrument also included indicators for the latent norm regarding bicycling. However, this information was only collected for 170 respondents, who had indicated that they had access to a bicycle on a regular basis. The later analysis showed that the data used for the estimation of the latent class and latent variable model could not support the inclusion of the latent norm construct regarding bicycling because of the relatively few observations of the corresponding indicators. Hence, we omit these indicators in the further analysis.

  6. A similar question format is employed by Molin et al. (2016). We agree with the authors that other data sources such as trip diary data could be considered to limit the impact of potential biases resulting from respondents’ self-reporting of aggregate frequencies of their own behaviour. Yet, an advantage of this question type over other data sources concerns the fact that self-reported mode-use frequencies can cover longer time periods than it is practically feasible using trip diary data (Molin et al. 2016).

  7. As the items were presented in an Australian context, the wording of some items was slightly modified, while the original meaning was preserved. In some cases, the items were generalised, as the target population of the present study differed from the context of Haustein’s (2012) original study, which targeted elderly people. For example, the item for the measurement of the construct “public transit enjoyment”, which is reported in Table 5, is not included in Haustein’s (2012) original study. However, it was added in the present study, as we were interested in measuring how the general population perceives the utility of public transit use.

  8. In the interest of brevity, we only present the most relevant estimation results. Additional estimation results of the measurement components of the latent normative belief submodels, the habit strengths submodels and the mode-specific attitude submodels are reported in the appendix.

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Acknowledgements

The authors are grateful to three anonymous reviewers, whose insightful comments helped to substantially improve an earlier version of this paper. However, the remaining errors are solely our own responsibility. RK and THR acknowledge support from the Australian Research Council under Grant LP150101266.

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Authors and Affiliations

  1. Research Centre for Integrated Transport Innovation, School of Civil and Environmental Engineering, UNSW Australia, Sydney, NSW, 2052, Australia

    Rico Krueger & Taha H. Rashidi

  2. Institute for Choice, University of South Australia, 140 Arthur Street, North Sydney, NSW, 2060, Australia

    Akshay Vij

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  1. Rico Krueger
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Appendix

Appendix

See Tables 14, 15, 16, and 17

Table 14 Estimates of coefficients on mode-use frequencies
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Table 15 Estimates of coefficient on modal habit strength scores
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Table 16 Estimates of measurement coefficients of the latent normative belief models
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Table 17 Estimates of coefficients for the latent mode-specific attitudes
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Krueger, R., Vij, A. & Rashidi, T.H. Normative beliefs and modality styles: a latent class and latent variable model of travel behaviour. Transportation 45, 789–825 (2018). https://doi.org/10.1007/s11116-016-9751-1

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