IDEAS home Printed from https://ideas.repec.org/a/spr/trosos/v12y2018i2d10.1007_s12626-018-0025-6.html
   My bibliography  Save this article

A Statistical Analysis Between Consumer Behavior and a Social Network Service: A Case Study of Used-Car Demand Following the Great East Japan Earthquake and Tsunami of 2011

Author

Listed:
  • Yuya Shibuya

    (The University of Tokyo)

  • Hideyuki Tanaka

    (The University of Tokyo)

Abstract

When a large-scale disaster hits a community, especially a water-related disaster, there is a scarcity of automobiles and a sudden increase in the demand for used cars in the damaged areas. This paper conducts a case study of a recent massive natural disaster, the Great East Japan Earthquake and Tsunami of 2011 to understand those car scarcities and demand in the aftermath of the catastrophe. We analyze the reasons for the increase in demand for used cars and how social media can predict people’s demand for used automobiles. In other words, this paper explores whether social media data can be used as a sensor of socio-economic recovery status in damaged areas during large-scale water-related disaster-recovery phases. For this purpose, we use social media communication as a proxy for estimating indicators of people’s activities in the real world. This study conducts both qualitative analysis and quantitative analysis. For the qualitative research, we carry out semi-structured interviews with used-car dealers in the tsunami-stricken area and unveil why people in the area demanded used cars. For the quantitative analysis, we collected Facebook page communication data and used-car market data before and after the Great East Japan Earthquake and Tsunami of 2011. By combining and analyzing these two types of data, we find that social media communication correlates with people’s activities in the real world. Furthermore, this study suggests that different types of communication on social media have different types of correlations with people’s activities. More precisely, we find that social media communication related to people’s activities for rebuilding and for emotional support is positively correlated with the demand for used cars after the Great East Japan Earthquake and Tsunami. On the other hand, communication about anxiety and information seeking correlates negatively with the demand for used cars.

Suggested Citation

  • Yuya Shibuya & Hideyuki Tanaka, 2018. "A Statistical Analysis Between Consumer Behavior and a Social Network Service: A Case Study of Used-Car Demand Following the Great East Japan Earthquake and Tsunami of 2011," The Review of Socionetwork Strategies, Springer, vol. 12(2), pages 205-236, December.
    • Handle: RePEc:spr:trosos:v:12:y:2018:i:2:d:10.1007_s12626-018-0025-6
    DOI: 10.1007/s12626-018-0025-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12626-018-0025-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12626-018-0025-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Xiangyang Guan & Cynthia Chen, 2014. "Using social media data to understand and assess disasters," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 74(2), pages 837-850, November.
    2. Cheng, John W. & Mitomo, Hitoshi & Otsuka, Tokio & Jeon, Stefan Y., 2015. "The effects of ICT and mass media in post-disaster recovery – A two model case study of the Great East Japan Earthquake," Telecommunications Policy, Elsevier, vol. 39(6), pages 515-532.
    Full references (including those not matched with items on IDEAS)

    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. Sandulika Abesinghe & Nayomi Kankanamge & Tan Yigitcanlar & Surabhi Pancholi, 2023. "Image of a City through Big Data Analytics: Colombo from the Lens of Geo-Coded Social Media Data," Future Internet, MDPI, vol. 15(1), pages 1-21, January.
    2. Cheng, John W. & Mitomo, Hitoshi, 2017. "Impact of media form on the perceived image of the television news media in the age of media convergence," 14th ITS Asia-Pacific Regional Conference, Kyoto 2017: Mapping ICT into Transformation for the Next Information Society 168479, International Telecommunications Society (ITS).
    3. Rachel Samuels & Jiajia Xie & Neda Mohammadi & John E. Taylor, 2022. "Tipping the scales: how geographical scale affects the interpretation of social media behavior in crisis research," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 112(1), pages 545-564, May.
    4. Rachel Samuels & John E. Taylor & Neda Mohammadi, 2020. "Silence of the Tweets: incorporating social media activity drop-offs into crisis detection," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 103(1), pages 1455-1477, August.
    5. Yandong Wang & Teng Wang & Xinyue Ye & Jianqi Zhu & Jay Lee, 2015. "Using Social Media for Emergency Response and Urban Sustainability: A Case Study of the 2012 Beijing Rainstorm," Sustainability, MDPI, vol. 8(1), pages 1-17, December.
    6. Muhammad Ashraf Fauzi, 2023. "Social media in disaster management: review of the literature and future trends through bibliometric analysis," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 118(2), pages 953-975, September.
    7. S M Nadim Sultan & Keshav Lall Maharjan, 2022. "Cyclone-Induced Disaster Loss Reduction by Social Media: A Case Study on Cyclone Amphan in Koyra Upazila, Khulna District, Bangladesh," Sustainability, MDPI, vol. 14(21), pages 1-17, October.
    8. Clemens Havas & Bernd Resch, 2021. "Portability of semantic and spatial–temporal machine learning methods to analyse social media for near-real-time disaster monitoring," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 108(3), pages 2939-2969, September.
    9. Ji-Wan Lee & Chung-Gil Jung & Jee-Hun Chung & Seong-Joon Kim, 2019. "The relationship among meteorological, agricultural, and in situ news-generated big data on droughts," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 98(2), pages 765-781, September.
    10. Sarah Gardiner & Jinyan Chen & Margarida Abreu Novais & Karine Dupré & J. Guy Castley, 2023. "Analyzing and Leveraging Social Media Disaster Communication of Natural Hazards: Community Sentiment and Messaging Regarding the Australian 2019/20 Bushfires," Societies, MDPI, vol. 13(6), pages 1-20, May.
    11. Xiaoxue Cheng & Guifeng Han & Yifan Zhao & Lin Li, 2019. "Evaluating Social Media Response to Urban Flood Disaster: Case Study on an East Asian City (Wuhan, China)," Sustainability, MDPI, vol. 11(19), pages 1-18, September.
    12. Md. Qamruzzaman & Salma Karim, 2020. "ICT Investment Impact on Human Capital Development through the Channel of Financial Development in Bangladesh: An Investigation of Quantile ARDL and Toda-Yamamoto Test," Academic Journal of Interdisciplinary Studies, Richtmann Publishing Ltd, vol. 9, September.
    13. Stieglitz, Stefan & Mirbabaie, Milad & Ross, Björn & Neuberger, Christoph, 2018. "Social media analytics – Challenges in topic discovery, data collection, and data preparation," International Journal of Information Management, Elsevier, vol. 39(C), pages 156-168.
    14. Bianca E. Lopez & Nicholas R. Magliocca & Andrew T. Crooks, 2019. "Challenges and Opportunities of Social Media Data for Socio-Environmental Systems Research," Land, MDPI, vol. 8(7), pages 1-18, July.
    15. Sachin Modgil & Rohit Kumar Singh & Cyril Foropon, 2022. "Quality management in humanitarian operations and disaster relief management: a review and future research directions," Annals of Operations Research, Springer, vol. 319(1), pages 1045-1098, December.
    16. Mahnoosh Hassankhani & Mehdi Alidadi & Ayyoob Sharifi & Abolghasem Azhdari, 2021. "Smart City and Crisis Management: Lessons for the COVID-19 Pandemic," IJERPH, MDPI, vol. 18(15), pages 1-18, July.
    17. Gabrielle Turner-McGrievy & Amir Karami & Courtney Monroe & Heather M. Brandt, 2020. "Dietary pattern recognition on Twitter: a case example of before, during, and after four natural disasters," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 103(1), pages 1035-1049, August.
    18. Martínez-Rojas, María & Pardo-Ferreira, María del Carmen & Rubio-Romero, Juan Carlos, 2018. "Twitter as a tool for the management and analysis of emergency situations: A systematic literature review," International Journal of Information Management, Elsevier, vol. 43(C), pages 196-208.
    19. Faxi Yuan & Rui Liu, 2018. "Crowdsourcing for forensic disaster investigations: Hurricane Harvey case study," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 93(3), pages 1529-1546, September.
    20. Kelton Minor & Esteban Moro & Nick Obradovich, 2023. "Adverse weather amplifies social media activity," Papers 2302.08456, arXiv.org.

    More about this item

    Keywords

    Used-car market; Social media; Disaster logistics; Big data;
    All these keywords.

    JEL classification:

    • H12 - Public Economics - - Structure and Scope of Government - - - Crisis Management
    • L62 - Industrial Organization - - Industry Studies: Manufacturing - - - Automobiles; Other Transportation Equipment; Related Parts and Equipment

    Statistics

    Access and download statistics

    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:spr:trosos:v:12:y:2018:i:2:d:10.1007_s12626-018-0025-6. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.