IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i6p3492-d521643.html
   My bibliography  Save this article

Investigating Regional and Generational Heterogeneity in Low-Carbon Travel Behavior Intention Based on a PLS-SEM Approach

Author

Listed:
  • Wu Li

    (Faculty of Infrastructure Engineering, School of Transportation and Logistics, Dalian University of Technology, Dalian 116024, China)

  • Shengchuan Zhao

    (Faculty of Infrastructure Engineering, School of Transportation and Logistics, Dalian University of Technology, Dalian 116024, China)

  • Jingwen Ma

    (Faculty of Infrastructure Engineering, School of Transportation and Logistics, Dalian University of Technology, Dalian 116024, China)

  • Wenwen Qin

    (Faculty of Traffic Engineering, Kunming University of Science and Technology, Kunming 650504, China)

Abstract

This study aims at reviewing whether regional and generational differences exist in behavior intention to adopt low-carbon travel modes. Based on 759 questionnaires collected from three cities (Zhenjiang, Suzhou, and Shanghai) with different population sizes in China, we develop a modified theory of planned behavior (MTPB) model framework integrating low-carbon transport policies, psychological aspects, personal norms, and travel habits. A more advanced partial least-square method of structural equation model (PLS-SEM) and a multiple-group analysis (MGA) model are applied to estimate the effects and heterogeneities of these factors on low-carbon travel behavior intention among three cities and four age groups. The results show that the roles of low-carbon policies, subjective norms, and personal norms on behavior intention of adopting low-carbon travel modes are more salient. The effect of low-carbon policy on behavior is much weaker than it is on intention, and it does not follow that such intention will often be followed up with action. There is regional and generational heterogeneity in terms of the influence on low-carbon travel behavior intention. In particular, the benefits of low-carbon policies are more remarkable in the middle-sized city, young adult group, and pre-older adult group. The low-carbon travel behavior intention in the large-sized city, junior-middle adult group, and senior-middle adult group are affected by subjective norms more easily. The large-sized city and young adult group have better personal norms in favor of low-carbon travel. The findings could provide helpful insights into developing heterogeneous transport policies to encourage different travelers to switch from auto to low-carbon travel modes.

Suggested Citation

  • Wu Li & Shengchuan Zhao & Jingwen Ma & Wenwen Qin, 2021. "Investigating Regional and Generational Heterogeneity in Low-Carbon Travel Behavior Intention Based on a PLS-SEM Approach," Sustainability, MDPI, vol. 13(6), pages 1-18, March.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:6:p:3492-:d:521643
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/6/3492/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/6/3492/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ünal, Ayça Berfu & Steg, Linda & Granskaya, Juliana, 2019. "“To support or not to support, that is the question”. Testing the VBN theory in predicting support for car use reduction policies inRussia," Transportation Research Part A: Policy and Practice, Elsevier, vol. 119(C), pages 73-81.
    2. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 179-211, December.
    3. Ezlika M. Ghazali & Bang Nguyen & Dilip S. Mutum & Su-Fei Yap, 2019. "Pro-Environmental Behaviours and Value-Belief-Norm Theory: Assessing Unobserved Heterogeneity of Two Ethnic Groups," Sustainability, MDPI, vol. 11(12), pages 1-28, June.
    4. Liang, Jyun-Kai & Eccarius, Timo & Lu, Chung-Cheng, 2019. "Investigating factors that affect the intention to use shared parking: A case study of Taipei City," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 799-812.
    5. Wang, Zhaohua & Liu, Wei, 2015. "Determinants of CO2 emissions from household daily travel in Beijing, China: Individual travel characteristic perspectives," Applied Energy, Elsevier, vol. 158(C), pages 292-299.
    6. Schneider, Robert J., 2013. "Theory of routine mode choice decisions: An operational framework to increase sustainable transportation," Transport Policy, Elsevier, vol. 25(C), pages 128-137.
    7. Premkumar, G. & Bhattacherjee, Anol, 2008. "Explaining information technology usage: A test of competing models," Omega, Elsevier, vol. 36(1), pages 64-75, February.
    8. Liu, Diyi & Du, Huibin & Southworth, Frank & Ma, Shoufeng, 2017. "The influence of social-psychological factors on the intention to choose low-carbon travel modes in Tianjin, China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 105(C), pages 42-53.
    9. Friman, Margareta & Gärling, Tommy & Ettema, Dick & Olsson, Lars E., 2017. "How does travel affect emotional well-being and life satisfaction?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 106(C), pages 170-180.
    10. Xu, Bin & Lin, Boqiang, 2015. "Carbon dioxide emissions reduction in China's transport sector: A dynamic VAR (vector autoregression) approach," Energy, Elsevier, vol. 83(C), pages 486-495.
    11. Peng Jing & Gang Xu & Yuexia Chen & Yuji Shi & Fengping Zhan, 2020. "The Determinants behind the Acceptance of Autonomous Vehicles: A Systematic Review," Sustainability, MDPI, vol. 12(5), pages 1-26, February.
    12. Tenenhaus, Michel & Vinzi, Vincenzo Esposito & Chatelin, Yves-Marie & Lauro, Carlo, 2005. "PLS path modeling," Computational Statistics & Data Analysis, Elsevier, vol. 48(1), pages 159-205, January.
    13. Fu, Xuemei & Juan, Zhicai, 2017. "Exploring the psychosocial factors associated with public transportation usage and examining the “gendered” difference," Transportation Research Part A: Policy and Practice, Elsevier, vol. 103(C), pages 70-82.
    14. Allinson, David & Irvine, Katherine N. & Edmondson, Jill L. & Tiwary, Abhishek & Hill, Graeme & Morris, Jonathan & Bell, Margaret & Davies, Zoe G. & Firth, Steven K. & Fisher, Jill & Gaston, Kevin J. , 2016. "Measurement and analysis of household carbon: The case of a UK city," Applied Energy, Elsevier, vol. 164(C), pages 871-881.
    15. Wang, Shanyong & Li, Jun & Zhao, Dingtao, 2017. "The impact of policy measures on consumer intention to adopt electric vehicles: Evidence from China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 105(C), pages 14-26.
    16. Shi, Dan & Wang, Lei & Wang, Zhenxia, 2019. "What affects individual energy conservation behavior: Personal habits, external conditions or values? An empirical study based on a survey of college students," Energy Policy, Elsevier, vol. 128(C), pages 150-161.
    17. Jia, Ning & Li, Liying & Ling, Shuai & Ma, Shoufeng & Yao, Wang, 2018. "Influence of attitudinal and low-carbon factors on behavioral intention of commuting mode choice – A cross-city study in China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 111(C), pages 108-118.
    18. Chen, Shang-Yu, 2016. "Using the sustainable modified TAM and TPB to analyze the effects of perceived green value on loyalty to a public bike system," Transportation Research Part A: Policy and Practice, Elsevier, vol. 88(C), pages 58-72.
    19. Zhang, Chunqin & Juan, Zhicai & Lu, Weite & Xiao, Guangnian, 2016. "Do the organizational forms affect passenger satisfaction? Evidence from Chinese public transport service," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 129-148.
    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. Qi Chen & Yibo Yan & Xu Zhang & Jian Chen, 2022. "Impact of Subjective and Objective Factors on Subway Travel Behavior: Spatial Differentiation," IJERPH, MDPI, vol. 19(23), pages 1-17, November.
    2. An-Jin Shie & You-Yu Dai & Ming-Xing Shen & Li Tian & Ming Yang & Wen-Wei Luo & Yenchun Jim Wu & Zhao-Hui Su, 2022. "Diamond Model of Green Commitment and Low-Carbon Travel Motivation, Constraint, and Intention," IJERPH, MDPI, vol. 19(14), pages 1-21, July.
    3. Xiaofeng Ji & Haotian Guan & Mengyuan Lu & Fang Chen & Wenwen Qin, 2022. "International Research Progress in School Travel and Behavior: A Literature Review and Bibliometric Analysis," Sustainability, MDPI, vol. 14(14), pages 1-25, July.
    4. Bircan Arslannur & Ahmet Tortum, 2023. "Public Transport Modeling for Commuting in Cities with Different Development Levels Using Extended Theory of Planned Behavior," Sustainability, MDPI, vol. 15(15), pages 1-24, August.
    5. Liying Wang & Junya Wang & Pengxia Shen & Shangqing Liu & Shuwei Zhang, 2023. "Low-Carbon Travel Behavior in Daily Residence and Tourism Destination: Based on TPB-ABC Integrated Model," Sustainability, MDPI, vol. 15(19), pages 1-18, September.
    6. Yuhuan Xia & Yubo Liu & Changlin Han & Yang Gao & Yuanyuan Lan, 2022. "How Does Environmentally Specific Servant Leadership Fuel Employees’ Low-Carbon Behavior? The Role of Environmental Self-Accountability and Power Distance Orientation," IJERPH, MDPI, vol. 19(5), pages 1-17, March.
    7. Bin Wang & Qiuxia Zheng & Ao Sun & Jie Bao & Dianting Wu, 2021. "Spatio-Temporal Patterns of CO 2 Emissions and Influencing Factors in China Using ESDA and PLS-SEM," Mathematics, MDPI, vol. 9(21), pages 1-24, October.

    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. Schikofsky, Jan & Dannewald, Till & Kowald, Matthias, 2020. "Exploring motivational mechanisms behind the intention to adopt mobility as a service (MaaS): Insights from Germany," Transportation Research Part A: Policy and Practice, Elsevier, vol. 131(C), pages 296-312.
    2. Timmer, Sebastian & Bösehans, Gustav & Henkel, Sven, 2023. "Behavioural norms or personal gains? – An empirical analysis of commuters‘ intention to switch to multimodal mobility behaviour," Transportation Research Part A: Policy and Practice, Elsevier, vol. 170(C).
    3. Geng, Jichao & Long, Ruyin & Chen, Hong & Li, Wenbo, 2017. "Exploring the motivation-behavior gap in urban residents’ green travel behavior: A theoretical and empirical study," Resources, Conservation & Recycling, Elsevier, vol. 125(C), pages 282-292.
    4. Mao Ye & Yajing Chen & Guixin Yang & Bo Wang & Qizhou Hu, 2020. "Mixed Logit Models for Travelers’ Mode Shifting Considering Bike-Sharing," Sustainability, MDPI, vol. 12(5), pages 1-18, March.
    5. Bin Wang & Jionghua Li & Ao Sun & Yongming Wang & Dianting Wu, 2019. "Residents’ Green Purchasing Intentions in a Developing-Country Context: Integrating PLS-SEM and MGA Methods," Sustainability, MDPI, vol. 12(1), pages 1-21, December.
    6. Adu-Gyamfi, Gibbson & Song, Huaming & Asamoah, Ama Nyarko & Li, Liang & Nketiah, Emmanuel & Obuobi, Bright & Adjei, Mavis & Cudjoe, Dan, 2022. "Towards sustainable vehicular transport: Empirical assessment of battery swap technology adoption in China," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    7. Jun Li & Jiachao Shen & Bicen Jia, 2021. "Exploring Intention to Use Shared Electric Bicycles by the Extended Theory of Planned Behavior," Sustainability, MDPI, vol. 13(8), pages 1-13, April.
    8. Ma, Liang & Zhang, Xin & Ding, Xiaoyan & Wang, Gaoshan, 2018. "Bike sharing and users’ subjective well-being: An empirical study in China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 118(C), pages 14-24.
    9. Adu-Gyamfi, Gibbson & Song, Huaming & Obuobi, Bright & Nketiah, Emmanuel & Wang, Hong & Cudjoe, Dan, 2022. "Who will adopt? Investigating the adoption intention for battery swap technology for electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    10. Ning, Yu & Yan, Mian & Xu, Su Xiu & Li, Yina & Li, Lixu, 2021. "Shared parking acceptance under perceived network externality and risks: Theory and evidence," Transportation Research Part A: Policy and Practice, Elsevier, vol. 150(C), pages 1-15.
    11. Qingyin Li & Meilin Dai & Yongli Zhang & Rong Wu, 2023. "The Effect of Public Traffic Accessibility on the Low-Carbon Awareness of Residents in Guangzhou: The Perspective of Travel Behavior," Land, MDPI, vol. 12(10), pages 1-20, October.
    12. Zhang, Xin & Zhong, Shiquan & Ling, Shuai & Jia, Ning & Qi, Hang & He, Zhengbing, 2022. "How to promote the transition from solo driving to mobility services delivery? An empirical study focusing on ridesharing," Transport Policy, Elsevier, vol. 129(C), pages 176-187.
    13. Wei, Jia & Chen, Hong & Cui, Xiaotong & Long, Ruyin, 2016. "Carbon capability of urban residents and its structure: Evidence from a survey of Jiangsu Province in China," Applied Energy, Elsevier, vol. 173(C), pages 635-649.
    14. Hyeongjin Ahn & Eunil Park, 2022. "For sustainable development in the transportation sector: Determinants of acceptance of sustainable transportation using the innovation diffusion theory and technology acceptance model," Sustainable Development, John Wiley & Sons, Ltd., vol. 30(5), pages 1169-1183, October.
    15. Chaouali, Walid & Souiden, Nizar & Ladhari, Riadh, 2017. "Explaining adoption of mobile banking with the theory of trying, general self-confidence, and cynicism," Journal of Retailing and Consumer Services, Elsevier, vol. 35(C), pages 57-67.
    16. Lin, Boqiang & Wang, Xia, 2021. "Does low-carbon travel intention really lead to actual low-carbon travel? Evidence from urban residents in China," Economic Analysis and Policy, Elsevier, vol. 72(C), pages 743-756.
    17. Jaiswal, Deepak & Kaushal, Vikrant & Kant, Rishi & Kumar Singh, Pankaj, 2021. "Consumer adoption intention for electric vehicles: Insights and evidence from Indian sustainable transportation," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    18. Debora Bettiga & Lucio Lamberti & Emanuele Lettieri, 2020. "Individuals’ adoption of smart technologies for preventive health care: a structural equation modeling approach," Health Care Management Science, Springer, vol. 23(2), pages 203-214, June.
    19. Garfield Wayne Hunter & Gideon Sagoe & Daniele Vettorato & Ding Jiayu, 2019. "Sustainability of Low Carbon City Initiatives in China: A Comprehensive Literature Review," Sustainability, MDPI, vol. 11(16), pages 1-37, August.
    20. Yu Wang & Shanyong Wang & Jing Wang & Jiuchang Wei & Chenglin Wang, 2020. "An empirical study of consumers’ intention to use ride-sharing services: using an extended technology acceptance model," Transportation, Springer, vol. 47(1), pages 397-415, February.

    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:13:y:2021:i:6:p:3492-:d:521643. 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.