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Value‐centric analysis of user adoption for sustainable urban micro‐mobility transportation through shared e‐scooter services

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  • Levent Çallı
  • Büşra Alma Çallı

Abstract

Micro‐mobility services, which are considered a sustainable alternative to traditional transportation modes, have gained substantial popularity due to advancements in mobile technology. As one of those modes of transportation, shared e‐scooter services have encouraged several startups in urban areas, allowing them to reach massive numbers of consumers in a highly competitive environment. This study aims to explore gains and barriers that affect the intention of consumers to use shared e‐scooter services, all within the framework of sustainability‐driven considerations. The Latent Dirichlet Allocation (LDA) Algorithm was used to analyse 24.798 reviews from the Google Play Store, uncovering eight topics. Those topics were used to discover customer value perceptions in the shared e‐scooter context and compare them with the related literature on perceived value. Besides, their impact on user ratings on the mobile application platform was measured using machine learning algorithms. The study's findings are expected to contribute to developing regulations for shared e‐scooter services, which have gained popularity as an eco‐friendly mode of transportation sustainability in urban areas by introducing a novel perspective.

Suggested Citation

  • Levent Çallı & Büşra Alma Çallı, 2024. "Value‐centric analysis of user adoption for sustainable urban micro‐mobility transportation through shared e‐scooter services," Sustainable Development, John Wiley & Sons, Ltd., vol. 32(6), pages 6408-6433, December.
    • Handle: RePEc:wly:sustdv:v:32:y:2024:i:6:p:6408-6433
    DOI: 10.1002/sd.3032
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    References listed on IDEAS

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    1. Hugo Badia & Erik Jenelius, 2023. "Shared e-scooter micromobility: review of use patterns, perceptions and environmental impacts," Transport Reviews, Taylor & Francis Journals, vol. 43(5), pages 811-837, September.
    2. Na Rang Kim & Soon Goo Hong, 2020. "Text mining for the evaluation of public services: the case of a public bike-sharing system," Service Business, Springer;Pan-Pacific Business Association, vol. 14(3), pages 315-331, September.
    3. Alberica Domitilla Bozzi & Anne Aguilera, 2021. "Shared E-Scooters: A Review of Uses, Health and Environmental Impacts, and Policy Implications of a New Micro-Mobility Service," Sustainability, MDPI, vol. 13(16), pages 1-17, August.
    4. Byoungsoo Kim & Daekil Kim, 2020. "Exploring the Key Antecedents Influencing Consumer’s Continuance Intention toward Bike-Sharing Services: Focus on China," IJERPH, MDPI, vol. 17(12), pages 1-14, June.
    5. Feinerer, Ingo & Hornik, Kurt & Meyer, David, 2008. "Text Mining Infrastructure in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i05).
    6. Nick Guenther & Matthias Schonlau, 2016. "Support vector machines," Stata Journal, StataCorp LLC, vol. 16(4), pages 917-937, December.
    7. Kailai Wang & Xiaodong Qian & Dillon Taylor Fitch & Yongsung Lee & Jai Malik & Giovanni Circella, 2023. "What travel modes do shared e-scooters displace? A review of recent research findings," Transport Reviews, Taylor & Francis Journals, vol. 43(1), pages 5-31, January.
    8. Aldred, Rachel & Jungnickel, Katrina, 2014. "Why culture matters for transport policy: the case of cycling in the UK," Journal of Transport Geography, Elsevier, vol. 34(C), pages 78-87.
    9. Gérard Biau & Erwan Scornet, 2016. "A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 197-227, June.
    10. Hyunsun Park & Shan Jiang & One-Ki Daniel Lee & Younghoon Chang, 2024. "Exploring the Attractiveness of Service Robots in the Hospitality Industry: Analysis of Online Reviews," Information Systems Frontiers, Springer, vol. 26(1), pages 41-61, February.
    11. Samadzad, Mahdi & Nosratzadeh, Hossein & Karami, Hossein & Karami, Ali, 2023. "What are the factors affecting the adoption and use of electric scooter sharing systems from the end user's perspective?," Transport Policy, Elsevier, vol. 136(C), pages 70-82.
    12. Mohammed Hamad Almannaa & Faisal Adnan Alsahhaf & Huthaifa I. Ashqar & Mohammed Elhenawy & Mahmoud Masoud & Andry Rakotonirainy, 2021. "Perception Analysis of E-Scooter Riders and Non-Riders in Riyadh, Saudi Arabia: Survey Outputs," Sustainability, MDPI, vol. 13(2), pages 1-24, January.
    13. Gérard Biau & Erwan Scornet, 2016. "Rejoinder on: A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 264-268, June.
    14. Lucini, Filipe R. & Tonetto, Leandro M. & Fogliatto, Flavio S. & Anzanello, Michel J., 2020. "Text mining approach to explore dimensions of airline customer satisfaction using online customer reviews," Journal of Air Transport Management, Elsevier, vol. 83(C).
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