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Car pride and its behavioral implications: an exploration in Shanghai

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Abstract

Beyond their functional purpose, cars are often considered a status symbol. There may exist a certain level of pride associated with owning and using cars, particularly in regions where motorization is rapidly growing. However, there is little empirical evidence in terms of how car pride is related to different behavioral aspects, such as car ownership and use, especially in the context of developing countries. This paper presents an exploration of car pride and its association with car-related behavior. In this work, car pride is defined as the self-conscious emotion derived from the appraisal of owning and using cars as a positive self-representation. It pertains to both the symbolic and affective functions of the car. Using survey data (n = 1389) from Shanghai, China, we empirically measure car pride as a latent variable based on five Likert-scale statements and test the association of car pride with car use, vehicle preferences, and car ownership. Based on two structural equation models, we show that: (1) car pride is positively correlated with car use; (2) car pride correlates significantly with owning newer, more expensive, and luxury cars, and Shanghai’s more expensive local car licenses; (3) car owners in general have higher car pride than non-owners; and (4) car pride is largely independent of one’s socio-economic characteristics. Although the analysis focuses on Shanghai, the findings of the positive correlation between car pride and behavior are consistent with prior studies in developed countries. These findings highlight the importance of car pride regarding multiple behavioral aspects of car ownership and use and its potential impact on mobility management.

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Notes

  1. Root mean square error of approximation (RMSEA), comparative fit index (CFI), and Tucker-Lewis index (TLI) are commonly used model fit indices and preferred for one-time analyses (Schreiber et al. 2006). RMSEA represents the square root of the average of the covariance residuals; zero represents a perfect fit, but the maximum is unlimited. Both CFI and TLI are based on comparison against a baseline (independence) model. They roughly represent the extent to which the model of interest is better than the independence model; values that approach 1 indicate acceptable fit. In particular, TLI tends to be lower if the model is complex. The rule of thumb cutoff criterion for model selection are (1) RMSEA < 0.06, (2) CFI ≥ 0.95, and (3) TLI ≥ 0.95 (Hu and Bentler 1999). However, these criteria are merely guidelines, and it has also been shown that they may over-reject true models at small size and thus are less preferable when sample size is small. For Model 1, we find that the model fit is reasonably good in terms of RMSEA and CFI, but poor in TLI. This may be because of the relatively smaller sample size (compared to Model 2) and more complex model structure.

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA, 02139, USA

    Zhan Zhao

  2. Department of Urban Studies and Planning, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA, 02139, USA

    Jinhua Zhao

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  1. Zhan Zhao
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  2. Jinhua Zhao
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Correspondence to Jinhua Zhao.

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Appendices

Appendix A: Sample statistics

In Table 6, we show the comparison of sample and city statistics regarding some key socio-economic variables. Note that we deliberately oversampled car owners because we want to obtain adequate observations to estimate the influence of car pride on car use and vehicle choice. Also, our sample skews toward population with higher education, which is likely a result of using online surveys. To address these issues, we apply iterative proportional fitting (IPF) to assign a weight to each sample response based on the city statistics. Also, as shown in our model results, socio-economic variables have minimal effect on car pride.

Table 6 Comparison of sample distribution and city statistics
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Appendix B: extended SEM results

There are three latent variables involved in Model 1—PRIDE, PT_ACC1, and PT_ACC2. The estimated coefficients for their measurement equations are summarized in Table 7. All estimates are statistically significant. Note that all the variables used for measuring car pride are highly correlated. Both PT_ACC1 and PT_ACC2 represent transit accessibility. The difference is that the former is an objective measure of transit accessibility at home, while the latter is a subjective measure of general transit accessibility.

Table 7 Estimates of measurement equations in Model 1
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Zhao, Z., Zhao, J. Car pride and its behavioral implications: an exploration in Shanghai. Transportation 47, 793–810 (2020). https://doi.org/10.1007/s11116-018-9917-0

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