IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i17p4813-d263788.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/17/4813/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/11/17/4813/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. Walker, Joan & Ben-Akiva, Moshe, 2002. "Generalized random utility model," Mathematical Social Sciences, Elsevier, vol. 43(3), pages 303-343, July.
    5. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, September.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Magnus Moglia & John Hopkins & Anne Bardoel, 2021. "Telework, Hybrid Work and the United Nation’s Sustainable Development Goals: Towards Policy Coherence," Sustainability, MDPI, vol. 13(16), pages 1-28, August.
    2. Woo Jang & Fei Yuan & Jose Javier Lopez, 2021. "Investigating Sustainable Commuting Patterns by Socio-Economic Factors," Sustainability, MDPI, vol. 13(4), pages 1-14, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Schmid, Basil & Axhausen, Kay W., 2019. "In-store or online shopping of search and experience goods: A hybrid choice approach," Journal of choice modelling, Elsevier, vol. 31(C), pages 156-180.
    2. Malte Welling & Ewa Zawojska & Julian Sagebiel, 2022. "Information, Consequentiality and Credibility in Stated Preference Surveys: A Choice Experiment on Climate Adaptation," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 82(1), pages 257-283, May.
    3. Daina, Nicolò & Sivakumar, Aruna & Polak, John W., 2017. "Modelling electric vehicles use: a survey on the methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 447-460.
    4. Weibo Li & Maria Kamargianni, 2020. "An Integrated Choice and Latent Variable Model to Explore the Influence of Attitudinal and Perceptual Factors on Shared Mobility Choices and Their Value of Time Estimation," Transportation Science, INFORMS, vol. 54(1), pages 62-83, January.
    5. Gao, Kun & Sun, Lijun & Yang, Ying & Meng, Fanyu & Qu, Xiaobo, 2021. "Cumulative prospect theory coupled with multi-attribute decision making for modeling travel behavior," Transportation Research Part A: Policy and Practice, Elsevier, vol. 148(C), pages 1-21.
    6. Schmid, Basil & Jokubauskaite, Simona & Aschauer, Florian & Peer, Stefanie & Hössinger, Reinhard & Gerike, Regine & Jara-Diaz, Sergio R. & Axhausen, Kay W., 2019. "A pooled RP/SP mode, route and destination choice model to investigate mode and user-type effects in the value of travel time savings," Transportation Research Part A: Policy and Practice, Elsevier, vol. 124(C), pages 262-294.
    7. Enam, Annesha & Konduri, Karthik C. & Pinjari, Abdul R. & Eluru, Naveen, 2018. "An integrated choice and latent variable model for multiple discrete continuous choice kernels: Application exploring the association between day level moods and discretionary activity engagement choi," Journal of choice modelling, Elsevier, vol. 26(C), pages 80-100.
    8. Fernández-Antolín, Anna & Guevara, C. Angelo & de Lapparent, Matthieu & Bierlaire, Michel, 2016. "Correcting for endogeneity due to omitted attitudes: Empirical assessment of a modified MIS method using RP mode choice data," Journal of choice modelling, Elsevier, vol. 20(C), pages 1-15.
    9. Kroesen, Maarten & Chorus, Caspar, 2020. "A new perspective on the role of attitudes in explaining travel behavior: A psychological network model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 133(C), pages 82-94.
    10. Bernadeta Gołębiowska & Anna Bartczak & Mikołaj Czajkowski, 2020. "Energy Demand Management and Social Norms," Energies, MDPI, vol. 13(15), pages 1-20, July.
    11. Kingsley Adjenughwure & Basil Papadopoulos, 2019. "Towards a Fair and More Transparent Rule-Based Valuation of Travel Time Savings," Sustainability, MDPI, vol. 11(4), pages 1-19, February.
    12. Daziano, Ricardo A., 2015. "Inference on mode preferences, vehicle purchases, and the energy paradox using a Bayesian structural choice model," Transportation Research Part B: Methodological, Elsevier, vol. 76(C), pages 1-26.
    13. Isler, Cassiano Augusto & Blumenfeld, Marcelo & Caldeira, Gabriel Pereira & Roberts, Clive, 2024. "Long-Distance railway mode choice in Brazil: Evidence from a discrete choice experiment," Research in Transportation Economics, Elsevier, vol. 104(C).
    14. Allen, Jaime & Muñoz, Juan Carlos & Rosell, Jordi, 2019. "Effect of a major network reform on bus transit satisfaction," Transportation Research Part A: Policy and Practice, Elsevier, vol. 124(C), pages 310-333.
    15. Hurtubia, Ricardo & Nguyen, My Hang & Glerum, Aurélie & Bierlaire, Michel, 2014. "Integrating psychometric indicators in latent class choice models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 64(C), pages 135-146.
    16. Kim, Sung Hoo & Mokhtarian, Patricia L., 2023. "Finite mixture (or latent class) modeling in transportation: Trends, usage, potential, and future directions," Transportation Research Part B: Methodological, Elsevier, vol. 172(C), pages 134-173.
    17. Schmid, Basil & Becker, Felix & Axhausen, Kay W. & Widmer, Paul & Stein, Petra, 2023. "A simultaneous model of residential location, mobility tool ownership and mode choice using latent variables," Transportation Research Part A: Policy and Practice, Elsevier, vol. 178(C).
    18. Bansal, Prateek & Kumar, Rajeev Ranjan & Raj, Alok & Dubey, Subodh & Graham, Daniel J., 2021. "Willingness to pay and attitudinal preferences of Indian consumers for electric vehicles," Energy Economics, Elsevier, vol. 100(C).
    19. Joan L. Walker & Moshe Ben-Akiva, 2011. "Advances in Discrete Choice: Mixture Models," Chapters, in: André de Palma & Robin Lindsey & Emile Quinet & Roger Vickerman (ed.), A Handbook of Transport Economics, chapter 8, Edward Elgar Publishing.
    20. Haghani, Milad & Sarvi, Majid & Shahhoseini, Zahra, 2015. "Accommodating taste heterogeneity and desired substitution pattern in exit choices of pedestrian crowd evacuees using a mixed nested logit model," Journal of choice modelling, Elsevier, vol. 16(C), pages 58-68.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:11:y:2019:i:17:p:4813-:d:263788. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.