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Roles of Psychological Resistance to Change Factors and Heterogeneity in Car Stickiness and Transit Loyalty in Mode Shift Behavior: A Hybrid Choice Approach

Author

Listed:
  • Kun Gao

    (Key Laboratory of Road and Traffic Engineering of the Ministry of Education, College of Transportation Engineering, Tongji University, Shanghai 201804, China)

  • Minhua Shao

    (Key Laboratory of Road and Traffic Engineering of the Ministry of Education, College of Transportation Engineering, Tongji University, Shanghai 201804, China)

  • Lijun Sun

    (Key Laboratory of Road and Traffic Engineering of the Ministry of Education, College of Transportation Engineering, Tongji University, Shanghai 201804, China)

Abstract

To support the scientific policy making and planning for promoting the share rate of sustainable public transit in urban areas of large metropolises, this study analyzes the influences of psychological resistance to change factors on commuters’ mode shift behavior while some external changes happen in the transport supplies. The heterogeneities in the car users’ stickiness to car and the metro users’ loyalty to metro are examined to support individual-specific travel behavior prediction. Web-scripted efficient experimental stated preference surveys including four commuting modes and three key factors are generated, and face-to-face interviews are conducted to collect reliable behavioral data. A hybrid choice approach, simultaneously considering the latent variables and quantitative level-of-service variables of different options, is employed for analysis. The results indicate that psychological resistance to change factors (routine seeking, cognitive rigidity, and emotion reaction) have significant and substantial influences on car users’ inclination to previously used commuting mode (i.e., car) in mode shift behavior. Car users with stronger routine seeking, stronger cognitive rigidity, and less emotion reaction show more predilection to car. Car users’ income level, gender, marital status, commuting distance, commuting time, license type, and flexible work time are found to partially explain the heterogeneity in car stickiness. In-vehicle crowding of public transit is a much more crucial factor for attracting car users to shift to public transit as compared to cost and travel time. Metro users with stronger routine seeking and less emotion reaction present a stronger inclination to metro in mode shift behavior. The influences of psychological resistance to change factors on metro users’ mode shift behavior are comparatively smaller than the influences of these factors on car users’ behavior. Metro users’ age, education level, commuting distance, commuting time, occupation, and flexible work time are identified to be associated with predilections for metro.

Suggested Citation

  • Kun Gao & Minhua Shao & Lijun Sun, 2019. "Roles of Psychological Resistance to Change Factors and Heterogeneity in Car Stickiness and Transit Loyalty in Mode Shift Behavior: A Hybrid Choice Approach," Sustainability, MDPI, vol. 11(17), pages 1-20, September.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:17:p:4813-:d:263788
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    1. Mary Tripsas & Giovanni Gavetti, 2000. "Capabilities, cognition, and inertia: evidence from digital imaging," Strategic Management Journal, Wiley Blackwell, vol. 21(10‐11), pages 1147-1161, October.
    2. Vij, Akshay & Walker, Joan L., 2016. "How, when and why integrated choice and latent variable models are latently useful," Transportation Research Part B: Methodological, Elsevier, vol. 90(C), pages 192-217.
    3. Kouwenhoven, Marco & de Jong, Gerard C. & Koster, Paul & van den Berg, Vincent A.C. & Verhoef, Erik T. & Bates, John & Warffemius, Pim M.J., 2014. "New values of time and reliability in passenger transport in The Netherlands," Research in Transportation Economics, Elsevier, vol. 47(C), pages 37-49.
    4. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555.
    5. Gang Cheng & Shuzhi Zhao & Jin Li, 2019. "The Effects of Latent Attitudinal Variables and Sociodemographic Differences on Travel Behavior in Two Small, Underdeveloped Cities in China," Sustainability, MDPI, vol. 11(5), pages 1-17, March.
    6. Peter Arcidiacono & Patrick Bayer & Jason R. Blevins & Paul B. Ellickson, 2016. "Estimation of Dynamic Discrete Choice Models in Continuous Time with an Application to Retail Competition," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 83(3), pages 889-931.
    7. Hess, Stephane & Train, Kenneth E. & Polak, John W., 2006. "On the use of a Modified Latin Hypercube Sampling (MLHS) method in the estimation of a Mixed Logit Model for vehicle choice," Transportation Research Part B: Methodological, Elsevier, vol. 40(2), pages 147-163, February.
    8. de Oña, Juan & de Oña, Rocío & Eboli, Laura & Mazzulla, Gabriella, 2013. "Perceived service quality in bus transit service: A structural equation approach," Transport Policy, Elsevier, vol. 29(C), pages 219-226.
    9. Steg, Linda, 2005. "Car use: lust and must. Instrumental, symbolic and affective motives for car use," Transportation Research Part A: Policy and Practice, Elsevier, vol. 39(2-3), pages 147-162.
    10. Liu, Shiyong & Triantis, Konstantinos P. & Sarangi, Sudipta, 2010. "A framework for evaluating the dynamic impacts of a congestion pricing policy for a transportation socioeconomic system," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(8), pages 596-608, October.
    11. Walker, Joan & Ben-Akiva, Moshe, 2002. "Generalized random utility model," Mathematical Social Sciences, Elsevier, vol. 43(3), pages 303-343, July.
    12. Rose, John M. & Bliemer, Michiel C.J. & Hensher, David A. & Collins, Andrew T., 2008. "Designing efficient stated choice experiments in the presence of reference alternatives," Transportation Research Part B: Methodological, Elsevier, vol. 42(4), pages 395-406, May.
    13. Dea van Lierop & Madhav G. Badami & Ahmed M. El-Geneidy, 2018. "What influences satisfaction and loyalty in public transport? A review of the literature," Transport Reviews, Taylor & Francis Journals, vol. 38(1), pages 52-72, January.
    14. Axhausen, Kay W. & Hess, Stephane & König, Arnd & Abay, Georg & Bates, John J. & Bierlaire, Michel, 2008. "Income and distance elasticities of values of travel time savings: New Swiss results," Transport Policy, Elsevier, vol. 15(3), pages 173-185, May.
    15. Ben-Akiva, Moshe & McFadden, Daniel & Train, Kenneth & Börsch-Supan, Axel, 2002. "Hybrid Choice Models: Progress and Challenges," Sonderforschungsbereich 504 Publications 02-29, Sonderforschungsbereich 504, Universität Mannheim;Sonderforschungsbereich 504, University of Mannheim.
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