IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v134y2018icp207-222.html
   My bibliography  Save this article

Technology and value network evolution in telehealth

Author

Listed:
  • Vesselkov, Alexandr
  • Hämmäinen, Heikki
  • Töyli, Juuso

Abstract

The wearable industry is growing and diverse. However, despite the variety in device producers and served purposes, many wearables include biosensors that can measure health parameters, such as heart rate. This makes them potentially useful or even disruptive for healthcare; particularly, for its remote delivery mode that is referred to as telehealth. Wearables and consumer technologies can bring changes to the current value network of telehealth industry by creating new business roles and attracting new stakeholders. However, traditionally regulated telehealth industry may be reluctant to accept unregulated non-medical devices. Furthermore, apart from the potential impact of wearables, the future of the industry is affected by other factors, which need to be understood. This article analyzes a potential evolution of the telehealth value network. For that, we first identified the current trends in the evolution of telehealth technologies and products based on the quantitative analysis and review of three different types of literature – scientific publications, patents, and press releases. Furthermore, we discussed the actors that can drive the future telehealth industry by taking a key role in its value network. The study indicates that technologies and products brought by consumer companies will be used in telehealth for the self-management of chronic diseases and wellness. To facilitate the interaction of the previously separated unregulated consumer and regulated medical domains of telehealth, a new health data aggregation role may emerge and take a central position in the value network. While several candidates for this role can be identified, currently, none of them has the full required expertise.

Suggested Citation

  • Vesselkov, Alexandr & Hämmäinen, Heikki & Töyli, Juuso, 2018. "Technology and value network evolution in telehealth," Technological Forecasting and Social Change, Elsevier, vol. 134(C), pages 207-222.
    • Handle: RePEc:eee:tefoso:v:134:y:2018:i:c:p:207-222
    DOI: 10.1016/j.techfore.2018.06.011
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162517309022
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2018.06.011?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. Kilkki, Kalevi & Mäntylä, Martti & Karhu, Kimmo & Hämmäinen, Heikki & Ailisto, Heikki, 2018. "A disruption framework," Technological Forecasting and Social Change, Elsevier, vol. 129(C), pages 275-284.
    2. Ferreira, Manuel Portugal & Santos, João Carvalho & de Almeida, Martinho Isnard Ribeiro & Reis, Nuno Rosa, 2014. "Mergers & acquisitions research: A bibliometric study of top strategy and international business journals, 1980–2010," Journal of Business Research, Elsevier, vol. 67(12), pages 2550-2558.
    3. Caviggioli, Federico, 2016. "Technology fusion: Identification and analysis of the drivers of technology convergence using patent data," Technovation, Elsevier, vol. 55, pages 22-32.
    4. Kindig, D.A. & Stoddart, G., 2003. "What is population health?," American Journal of Public Health, American Public Health Association, vol. 93(3), pages 380-383.
    5. Kim, Gabjo & Bae, Jinwoo, 2017. "A novel approach to forecast promising technology through patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 117(C), pages 228-237.
    6. Chang, Shu-Hao & Fan, Chin-Yuan, 2016. "Identification of the technology life cycle of telematics: A patent-based analytical perspective," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 1-10.
    7. Ling-Li Li & Guohua Ding & Nan Feng & Ming-Huang Wang & Yuh-Shan Ho, 2009. "Global stem cell research trend: Bibliometric analysis as a tool for mapping of trends from 1991 to 2006," Scientometrics, Springer;Akadémiai Kiadó, vol. 80(1), pages 39-58, July.
    8. Song, Chie Hoon & Elvers, David & Leker, Jens, 2017. "Anticipation of converging technology areas — A refined approach for the identification of attractive fields of innovation," Technological Forecasting and Social Change, Elsevier, vol. 116(C), pages 98-115.
    9. Breschi, Stefano & Lissoni, Francesco & Malerba, Franco, 2003. "Knowledge-relatedness in firm technological diversification," Research Policy, Elsevier, vol. 32(1), pages 69-87, January.
    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. Delgosha, Mohammad Soltani & Hajiheydari, Nastaran & Talafidaryani, Mojtaba, 2022. "Discovering IoT implications in business and management: A computational thematic analysis," Technovation, Elsevier, vol. 118(C).
    2. Chauhan, Ankur & Jakhar, Suresh Kumar & Jabbour, Charbel Jose Chiappetta, 2022. "Implications for sustainable healthcare operations in embracing telemedicine services during a pandemic," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    3. Jiang, Syuan-Yi, 2022. "Transition and innovation ecosystem – investigating technologies, focal actors, and institution in eHealth innovations," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    4. Martínez-Caro, Eva & Cegarra-Navarro, Juan Gabriel & Alfonso-Ruiz, Francisco Javier, 2020. "Digital technologies and firm performance: The role of digital organisational culture," Technological Forecasting and Social Change, Elsevier, vol. 154(C).
    5. Capponi, Giovanna & Corrocher, Nicoletta, 2022. "Patterns of collaboration in mHealth: A network analysis," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    6. Jeon, Eunji & Yoon, Naeun & Sohn, So Young, 2023. "Exploring new digital therapeutics technologies for psychiatric disorders using BERTopic and PatentSBERTa," Technological Forecasting and Social Change, Elsevier, vol. 186(PA).
    7. Chiang, Chang-Tang, 2024. "A systematic literature network analysis of green information technology for sustainability: Toward smart and sustainable livelihoods," Technological Forecasting and Social Change, Elsevier, vol. 199(C).

    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. Su, Hsin-Ning & Moaniba, Igam M., 2017. "Investigating the dynamics of interdisciplinary evolution in technology developments," Technological Forecasting and Social Change, Elsevier, vol. 122(C), pages 12-23.
    2. Serkan Altuntas & Zulfiye Erdogan & Turkay Dereli, 2020. "A clustering-based approach for the evaluation of candidate emerging technologies," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 1157-1177, August.
    3. Burmaoglu, Serhat & Sartenaer, Olivier & Porter, Alan, 2019. "Conceptual definition of technology emergence: A long journey from philosophy of science to science policy," Technology in Society, Elsevier, vol. 59(C).
    4. Sungho Son & Nam-Wook Cho, 2020. "Technology Fusion Characteristics in the Solar Photovoltaic Industry of South Korea: A Patent Network Analysis Using IPC Co-Occurrence," Sustainability, MDPI, vol. 12(21), pages 1-19, October.
    5. Nicola Melluso & Andrea Bonaccorsi & Filippo Chiarello & Gualtiero Fantoni, 2021. "Rapid detection of fast innovation under the pressure of COVID-19," Papers 2102.00197, arXiv.org.
    6. Martin Kalthaus, 2020. "Knowledge recombination along the technology life cycle," Journal of Evolutionary Economics, Springer, vol. 30(3), pages 643-704, July.
    7. Sasaki, Hajime & Sakata, Ichiro, 2021. "Identifying potential technological spin-offs using hierarchical information in international patent classification," Technovation, Elsevier, vol. 100(C).
    8. Aaldering, Lukas Jan & Leker, Jens & Song, Chie Hoon, 2019. "Uncovering the dynamics of market convergence through M&A," Technological Forecasting and Social Change, Elsevier, vol. 138(C), pages 95-114.
    9. Sajad Ashouri & Anne-Laure Mention & Kosmas X. Smyrnios, 2021. "Anticipation and analysis of industry convergence using patent-level indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5727-5758, July.
    10. Ki Hong Kim & Young Jae Han & Sugil Lee & Sung Won Cho & Chulung Lee, 2019. "Text Mining for Patent Analysis to Forecast Emerging Technologies in Wireless Power Transfer," Sustainability, MDPI, vol. 11(22), pages 1-24, November.
    11. Kim, Tae San & Sohn, So Young, 2020. "Machine-learning-based deep semantic analysis approach for forecasting new technology convergence," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    12. Zhao, Shengchao & Zeng, Deming & Li, Jian & Feng, Ke & Wang, Yao, 2023. "Quantity or quality: The roles of technology and science convergence on firm innovation performance," Technovation, Elsevier, vol. 126(C).
    13. Juite Wang & Tzu-Yen Hsu, 2023. "Early discovery of emerging multi-technology convergence for analyzing technology opportunities from patent data: the case of smart health," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(8), pages 4167-4196, August.
    14. Su, Yu-Shan & Huang, Hsini & Daim, Tugrul & Chien, Pan-Wei & Peng, Ru-Ling & Karaman Akgul, Arzu, 2023. "Assessing the technological trajectory of 5G-V2X autonomous driving inventions: Use of patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    15. Puccetti, Giovanni & Giordano, Vito & Spada, Irene & Chiarello, Filippo & Fantoni, Gualtiero, 2023. "Technology identification from patent texts: A novel named entity recognition method," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    16. Guerras-Martín, Luis Ángel & Ronda-Pupo, Guillermo Armando & Zúñiga-Vicente, José Ángel & Benito-Osorio, Diana, 2020. "Half a century of research on corporate diversification: A new comprehensive framework," Journal of Business Research, Elsevier, vol. 114(C), pages 124-141.
    17. Erzurumlu, S. Sinan & Pachamanova, Dessislava, 2020. "Topic modeling and technology forecasting for assessing the commercial viability of healthcare innovations," Technological Forecasting and Social Change, Elsevier, vol. 156(C).
    18. Wang, Zhinan & Porter, Alan L. & Wang, Xuefeng & Carley, Stephen, 2019. "An approach to identify emergent topics of technological convergence: A case study for 3D printing," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 723-732.
    19. Youngjae Choi & Sanghyun Park & Sungjoo Lee, 2021. "Identifying emerging technologies to envision a future innovation ecosystem: A machine learning approach to patent data," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5431-5476, July.
    20. Candiani, Juan Antonio & Gilsing, Victor & Mastrogiorgio, Mariano, 2022. "Technological entry in new niches: Diversity, crowding and generalism," Technovation, Elsevier, vol. 116(C).

    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:eee:tefoso:v:134:y:2018:i:c:p:207-222. 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: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .

    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.