Exploring Information Processing Behaviors of Consumers in the Middle of Their Kaiyu with Smartphone

  • Mamoru Imanishi
  • Kosuke Yamashiro
  • Masakuni Iwami
  • Saburo SaitoEmail author
Part of the New Frontiers in Regional Science: Asian Perspectives book series (NFRSASIPER, volume 19)


At a year-end sale held in the Tenmonkan district, the city center commercial district of Kagoshima City, Japan, we carried out a social experiment that attempted to measure the effect of information provision on visitors by using a smartphone application developed by FQBIC that was able to simultaneously record users’ positions and their interactions with information contents provided by the town such as flyers and the like. This study, as a first step, analyzes the logs obtained through this social experiment, which record the interactions between visitors and information provided by the town, and investigates what kinds of information contents and forms would most effectively induce visitors’ Kaiyu within the city center district.


Kaiyu Shop-around behavior Information provision Location information Smartphone app Information transaction Logs GPS Indoor Messaging System (IMES) Forms of information contents Shake Tap 


  1. 1.
    Imanishi M, Iwami M, Yamashiro K, Saito S (2014) Measurement and analysis of effects of providing information to visitors during a year-end sale at a city center commercial district. In: Papers of the 30th annual meeting of The Japan Association for Real Estate Sciences. pp 65–70. (in Japanese)Google Scholar
  2. 2.
    Nikkei (2013) Urban development by big data. The Nikkei newspaper. Morning Ed. Date: 2013/10/06/. p 1. (in Japanese)Google Scholar
  3. 3.
    Saito S (2012) Incorporating big data sciences into strategic town management: town equity researches and the future of real estate sciences. Jpn J Real Estate Sci 26:38–46. (in Japanese)CrossRefGoogle Scholar
  4. 4.
    Saito S (2012) Strategic town management and big data sciences: smart city and town equity. Statistics 63(9):10–19. (in Japanese)Google Scholar
  5. 5.
    Saito S (2013) Composing and distributing town equity indexes for promoting real estate Investment on urban areas: urban studies and real estate businesses in the era of big data. Real Estate Res 55:13–25. (in Japanese)Google Scholar
  6. 6.
    Saito S (2014) Analytics of shop-around behaviors enhances the value of town: big data and town equity. Urban Adv 62:20–29. (in Japanese)Google Scholar
  7. 7.
    The Geospatial Information Authority of Japan Fundamental Geospatial Data Site
  8. 8.
    People Flow Project (2018) Evacuation visualization. Access date: 2018/04/30/.
  9. 9.
    Jalan Research Center, Zenrin Datacom (2011) Sightseeing spot analysis using second generation location information. ToriMakashi (Terima kasih in Indonesian), No. 26. pp 4–9. (in Japanese)Google Scholar
  10. 10.
    Yamashiro K, Imanishi M, Iwami M, Saito S (2015) What kind of information provision most effectively induces Kaiyu? A social experiment using smartphones during a year-end sale, paper presented at the 52nd annual meeting of Japan Section of Regional Science Association International (JSRSAI). (in Japanese)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Mamoru Imanishi
    • 1
  • Kosuke Yamashiro
    • 1
  • Masakuni Iwami
    • 3
  • Saburo Saito
    • 2
    • 3
    Email author
  1. 1.Department of Business and EconomicsNippon Bunri UniversityOita CityJapan
  2. 2.Faculty of EconomicsFukuoka UniversityFukuokaJapan
  3. 3.Fukuoka University Institute of Quantitative Behavioral Informatics for City and Space Economy (FQBIC)FukuokaJapan

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