IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i7p4331-d786878.html
   My bibliography  Save this article

A Layered Adopter-Structure Model for the Download of COVID-19 Contact Tracing Apps: A System Dynamics Study for mHealth Penetration

Author

Listed:
  • Makoto Niwa

    (Graduate School of Technology Management, Ritsumeikan University, Ibaraki 567-8570, Japan
    Discovery Research Laboratories, Nippon Shinyaku Co., Ltd., Kyoto 601-8550, Japan)

  • Yeongjoo Lim

    (Faculty of Business Administration, Ritsumeikan University, Ibaraki 567-8570, Japan)

  • Shintaro Sengoku

    (Life Style by Design Research Unit, Institute for Future Initiatives, University of Tokyo, Tokyo 113-0033, Japan)

  • Kota Kodama

    (Graduate School of Technology Management, Ritsumeikan University, Ibaraki 567-8570, Japan
    Center for Research and Education on Drug Discovery, The Graduate School of Pharmaceutical Sciences, Hokkaido University, Sapporo 060-0812, Japan)

Abstract

(1) Background: Contact tracing and notification apps for coronavirus disease 2019 (COVID-19) are installed on smartphones and are intended to detect contact with another person’s device. A high installation rate is important for these apps to enable them to be effective countermeasures against the silent transmission of diseases. However, the installation rate varies among apps and regions and the penetration dynamics of these applications are unclear. (2) Methods: The download behavior of contact tracing applications was investigated using publicly available datasets. The increase in downloads was modeled using a system dynamics model derived from the product growth model. (3) Results: The imitation effects present in the traditional product growth model were not observed in COVID-19 contact tracing apps. The system dynamics model, without the imitation effect, identified the downloads of the Australian COVIDSafe app. The system dynamics model, with a layered adopter, identified the downloads of the Japanese tracing app COCOA. The spread of COVID-19 and overall anti-COVID-19 government intervention measures in response to the spread of infection seemed to result in an increase in downloads. (4) Discussion: The suggested layered structure of users implied that individualized promotion for each layer was important. Addressing the issues among users who are skeptical about adoption is pertinent for optimal penetration of the apps.

Suggested Citation

  • Makoto Niwa & Yeongjoo Lim & Shintaro Sengoku & Kota Kodama, 2022. "A Layered Adopter-Structure Model for the Download of COVID-19 Contact Tracing Apps: A System Dynamics Study for mHealth Penetration," IJERPH, MDPI, vol. 19(7), pages 1-15, April.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:7:p:4331-:d:786878
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/7/4331/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/7/4331/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Natsuko Tabata & Mai Tsukada & Kozue Kubo & Yuri Inoue & Reiko Miroku & Fumihiko Odashima & Koichiro Shiratori & Takashi Sekiya & Shintaro Sengoku & Hideaki Shiroyama & Hiromichi Kimura, 2022. "Living Lab for Citizens’ Wellness: A Case of Maintaining and Improving a Healthy Diet under the COVID-19 Pandemic," IJERPH, MDPI, vol. 19(3), pages 1-17, January.
    2. Ahmed Elmokashfi & Joakim Sundnes & Amund Kvalbein & Valeriya Naumova & Sven-Arne Reinemo & Per Magne Florvaag & Håkon Kvale Stensland & Olav Lysne, 2021. "Nationwide rollout reveals efficacy of epidemic control through digital contact tracing," Nature Communications, Nature, vol. 12(1), pages 1-8, December.
    3. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    4. Cobelli, Nicola & Cassia, Fabio & Burro, Roberto, 2021. "Factors affecting the choices of adoption/non-adoption of future technologies during coronavirus pandemic," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    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. Trieste, Leopoldo & Geisler, Elie & Turchetti, Giuseppe, 2022. "Columbus' egg and the engineer's effect in forecasting solutions adoption," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    2. Oscar Gutiérrez & Francisco Ruiz-Aliseda, 2011. "Real options with unknown-date events," Annals of Finance, Springer, vol. 7(2), pages 171-198, May.
    3. Shari, Babajide Epe & Dioha, Michael O. & Abraham-Dukuma, Magnus C. & Sobanke, Victor O. & Emodi, Nnaemeka V., 2022. "Clean cooking energy transition in Nigeria: Policy implications for Developing countries," Journal of Policy Modeling, Elsevier, vol. 44(2), pages 319-343.
    4. Cambier, Adrien & Chardy, Matthieu & Figueiredo, Rosa & Ouorou, Adam & Poss, Michael, 2022. "Optimizing subscriber migrations for a telecommunication operator in uncertain context," European Journal of Operational Research, Elsevier, vol. 298(1), pages 308-321.
    5. Tiruwork B. Tibebu & Eric Hittinger & Qing Miao & Eric Williams, 2024. "Adoption Model Choice Affects the Optimal Subsidy for Residential Solar," Energies, MDPI, vol. 17(3), pages 1-19, February.
    6. Simon P. Anderson & André de Palma, 2012. "Competition for attention in the Information (overload) Age," RAND Journal of Economics, RAND Corporation, vol. 43(1), pages 1-25, March.
    7. Van, Tien Linh Cao & Barthelmes, Lukas & Gnann, Till & Speth, Daniel & Kagerbauer, Martin, 2021. "Addressing the gaps in market diffusion modeling of electrical vehicles: A case study from Germany for the integration of environmental policy measures," Working Papers "Sustainability and Innovation" S05/2021, Fraunhofer Institute for Systems and Innovation Research (ISI).
    8. Ma, Peng, 2021. "Optimal generic and brand advertising efforts in a decentralized supply chain considering customer surplus," Journal of Retailing and Consumer Services, Elsevier, vol. 60(C).
    9. Sergio Currarini & Carmen Marchiori & Alessandro Tavoni, 2016. "Network Economics and the Environment: Insights and Perspectives," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 65(1), pages 159-189, September.
    10. Klingler, Anna-Lena & Luthander, Rasmus, 2018. "Market diffusion of residential PV and battery systems driven by self-consumption: A comparison of Sweden and Germany," Working Papers "Sustainability and Innovation" S18/2018, Fraunhofer Institute for Systems and Innovation Research (ISI).
    11. Robertson, Alastair & Soopramanien, Didier & Fildes, Robert, 2007. "A segment-based analysis of Internet service adoption among UK households," Technology in Society, Elsevier, vol. 29(3), pages 339-350.
    12. Edgardo Arturo Ayala Gaytán, 2009. "Social network externalities and price dispersion in online markets," Ensayos Revista de Economia, Universidad Autonoma de Nuevo Leon, Facultad de Economia, vol. 0(2), pages 1-28, November.
    13. Liberali, Guilherme & Gruca, Thomas S. & Nique, Walter M., 2011. "The effects of sensitization and habituation in durable goods markets," European Journal of Operational Research, Elsevier, vol. 212(2), pages 398-410, July.
    14. Chul-Yong Lee & Jongsu Lee, 2009. "Demand Forecasting in the Early Stage of the Technology's Life Cycle Using Bayesian update," TEMEP Discussion Papers 200903, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Apr 2009.
    15. Régis Chenavaz & Corina Paraschiv & Gabriel Turinici, 2017. "Dynamic Pricing of New Products in Competitive Markets: A Mean-Field Game Approach," Working Papers hal-01592958, HAL.
    16. Yanwen Wang & Chunhua Wu & Ting Zhu, 2019. "Mobile Hailing Technology and Taxi Driving Behaviors," Marketing Science, INFORMS, vol. 38(5), pages 734-755, September.
    17. Jakob Grazzini & Matteo G. Richiardi & Lisa Sella, 2013. "Analysis of Agent-based Models," LABORatorio R. Revelli Working Papers Series 135, LABORatorio R. Revelli, Centre for Employment Studies.
    18. Bessi, Alessandro & Guidolin, Mariangela & Manfredi, Piero, 2021. "The role of gas on future perspectives of renewable energy diffusion: Bridging technology or lock-in?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    19. White, Reilly & Marinakis, Yorgos & Islam, Nazrul & Walsh, Steven, 2020. "Is Bitcoin a currency, a technology-based product, or something else?," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    20. Shigeno, Hidenori & Matsuzaki, Taisuke & Ueki, Yasushi & Tsuji, Masatsugu, 2023. "The Effect of the Covid-19 Pandemic on the Innovation Process of Small and Medium-sized Regional Firms," 32nd European Regional ITS Conference, Madrid 2023: Realising the digital decade in the European Union – Easier said than done? 278018, International Telecommunications Society (ITS).

    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:jijerp:v:19:y:2022:i:7:p:4331-:d:786878. 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.