IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v8y2016i11p1211-d83579.html
   My bibliography  Save this article

Technology-Based New Service Idea Generation for Smart Spaces: Application of 5G Mobile Communication Technology

Author

Listed:
  • Seonkoo Jeong

    (Future Technology & Strategy Research Laboratory, Electronics and Telecommunications Research Institute, 218 Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea)

  • Yujin Jeong

    (Department of Industrial & Systems Engineering, Dongguk University, Pil-dong, 3 ga, Joong-gu, Seoul 04620, Korea)

  • Keeeun Lee

    (Department of Industrial & Systems Engineering, Dongguk University, Pil-dong, 3 ga, Joong-gu, Seoul 04620, Korea)

  • Sungjoo Lee

    (Department of Industrial Engineering, Ajou University, Wonchen-dong, Youngtong-gu, Suwon 16499, Korea)

  • Byungun Yoon

    (Department of Industrial & Systems Engineering, Dongguk University, Pil-dong, 3 ga, Joong-gu, Seoul 04620, Korea)

Abstract

Innovative technology has made it possible to dramatically change the social and economic environment. In particular, 5G mobile communication technology that radically improves the performance of current technology can renew urban infrastructure, public services, and citizens’ lives for the implementation of smart spaces. Although new services need to be generated by such innovative technology, existing technology-based approaches have mostly relied on the intuition of experts rather than a systematic approach. Thus, this paper aims to present a method and process by which technology-based new ideas using 5G mobile communication technology are generated to realize a connected environment by focusing on technological functions as well as customer value. First, the relationships among technology, value, and service are defined through morphology analysis. Second, service opportunities are identified by developing a transformed buyer-utility map in the smart space environment. After mapping the established services, candidate cells for a new service were identified as vacant cells in the map with the removal of technically unnecessary candidates based upon the pre-defined relationship. Third, a new service idea is generated by modifying/extending candidates concretely through an ERRC (Eliminate, Reduce, Raise, Create) framework. Value factors are determined in advance and shown in the As-Is value curve representing the current status. The current level in the curve is then compared at an industrial level and value factors are chosen to newly modify or create. As a result, the To-Be curve is established and leads to a new service idea. It can be regarded as a useful tool for mobile carriers to plan new business models for smart spaces with adequate technology and market feasibility.

Suggested Citation

  • Seonkoo Jeong & Yujin Jeong & Keeeun Lee & Sungjoo Lee & Byungun Yoon, 2016. "Technology-Based New Service Idea Generation for Smart Spaces: Application of 5G Mobile Communication Technology," Sustainability, MDPI, vol. 8(11), pages 1-25, November.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:11:p:1211-:d:83579
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/8/11/1211/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/8/11/1211/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Michele Cincera & Dirk Czarnitzki & Susanne Thorwarth, 2011. "Efficiency of public spending in support of R&D activities," Reflets et perspectives de la vie économique, De Boeck Université, vol. 0(1), pages 131-139.
    2. Barry Wellman, 2001. "Physical Place and Cyberplace: The Rise of Personalized Networking," International Journal of Urban and Regional Research, Wiley Blackwell, vol. 25(2), pages 227-252, June.
    3. Sheehan, Norman T. & Bruni-Bossio, Vince, 2015. "Strategic value curve analysis: Diagnosing and improving customer value propositions," Business Horizons, Elsevier, vol. 58(3), pages 317-324.
    4. Giada Di Stefano & Alfonso Gambardella & Gianmario Verona, 2012. "Technology Push and Demand Pull Perspectives in Innovation Studies: Current Findings and Future Research Directions," Post-Print hal-00696607, HAL.
    5. Andreas Eggert & Wolfgang Ulaga, 2002. "Customer perceived value: a substitute for satisfaction in business markets?," Post-Print hal-00484980, HAL.
    6. An, Hyoung Joon & Ahn, Sang-Jin, 2016. "Emerging technologies—beyond the chasm: Assessing technological forecasting and its implication for innovation management in Korea," Technological Forecasting and Social Change, Elsevier, vol. 102(C), pages 132-142.
    7. Walsh, Vivien, 1996. "Design, innovation and the boundaries of the firm," Research Policy, Elsevier, vol. 25(4), pages 509-529, June.
    8. Di Stefano, Giada & Gambardella, Alfonso & Verona, Gianmario, 2012. "Technology push and demand pull perspectives in innovation studies: Current findings and future research directions," Research Policy, Elsevier, vol. 41(8), pages 1283-1295.
    9. Chi Leung & Yue Kwok, 2011. "Real options game analysis of sleeping patents," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 34(1), pages 41-65, May.
    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. Xiaolong Liu & Wei-Bin Lee & Quy-Anh Bui & Chia-Chen Lin & Hsiao-Ling Wu, 2018. "Biometrics-Based RSA Cryptosystem for Securing Real-Time Communication," Sustainability, MDPI, vol. 10(10), pages 1-15, October.
    2. Mingyu Park & Youngjung Geum, 2021. "On the data-driven generation of new service idea: integrated approach of morphological analysis and text mining," Service Business, Springer;Pan-Pacific Business Association, vol. 15(3), pages 539-561, September.

    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. Carolina Castaldi & Kyriakos Drivas, 2023. "Relatedness, Cross-relatedness and Regional Innovation Specializations: An Analysis of Technology, Design, and Market Activities in Europe and the US," Economic Geography, Taylor & Francis Journals, vol. 99(3), pages 253-284, May.
    2. Christoph P. Kiefer & Pablo Del Río González & Javier Carrillo‐Hermosilla, 2019. "Drivers and barriers of eco‐innovation types for sustainable transitions: A quantitative perspective," Business Strategy and the Environment, Wiley Blackwell, vol. 28(1), pages 155-172, January.
    3. Hötte, Kerstin, 2023. "Demand-pull, technology-push, and the direction of technological change," Research Policy, Elsevier, vol. 52(5).
    4. Uzunca, Bilgehan & Sharapov, Dmitry & Tee, Richard, 2022. "Governance rigidity, industry evolution, and value capture in platform ecosystems," Research Policy, Elsevier, vol. 51(7).
    5. Attila Havas, 2016. "Social and Business Innovations: Are Common Measurement Approaches Possible?," Foresight-Russia Форсайт, CyberLeninka;Федеральное государственное автономное образовательное учреждение высшего образования «Национальный исследовательский университет «Высшая школа экономики», vol. 10(2 (eng)), pages 58-80.
    6. Abdelfeteh Bitat, 2018. "Environmental regulation and eco-innovation: the Porter hypothesis refined," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 8(3), pages 299-321, September.
    7. Kim, Juran & Kang, Seungmook & Lee, Ki Hoon, 2021. "Evolution of digital marketing communication: Bibliometric analysis and network visualization from key articles," Journal of Business Research, Elsevier, vol. 130(C), pages 552-563.
    8. Antonelli, Cristiano & Gehringer, Agnieszka, 2015. "Knowledge externalities and demand pull: The European evidence," Economic Systems, Elsevier, vol. 39(4), pages 608-631.
    9. Chi, Maomao & Wang, Weijun & Lu, Xinyuan & George, Joey F., 2018. "Antecedents and outcomes of collaborative innovation capabilities on the platform collaboration environment," International Journal of Information Management, Elsevier, vol. 43(C), pages 273-283.
    10. Aurora A. C. Teixeira & Pedro Cosme Vieira & Ana Patrícia Abreu, 2017. "Sleeping Beauties and their princes in innovation studies," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(2), pages 541-580, February.
    11. Christian Brodhag, 2013. "Research universities, technology transfer, and job creation: what infrastructure, for what training?," Post-Print emse-00804454, HAL.
    12. Migliori, Stefania & De Massis, Alfredo & Maturo, Fabrizio & Paolone, Francesco, 2020. "How does family management affect innovation investment propensity? The key role of innovation impulses," Journal of Business Research, Elsevier, vol. 113(C), pages 243-256.
    13. José García-Quevedo & Gabriele Pellegrino & Maria Savona, 2017. "Reviving demand-pull perspectives: The effect of demand uncertainty and stagnancy on R&D strategy," Cambridge Journal of Economics, Oxford University Press, vol. 41(4), pages 1087-1122.
    14. Vahid Omidi & Abolfazl Shahabadi & Nader Mehregan, 2020. "Innovation Drivers in Developing Countries," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 11(2), pages 707-720, June.
    15. Pinho, Celso R.A. & Pinho, Maria Luiza C.A. & Deligonul, Seyda Z. & Tamer Cavusgil, S., 2022. "The agility construct in the literature: Conceptualization and bibliometric assessment," Journal of Business Research, Elsevier, vol. 153(C), pages 517-532.
    16. Chiara Castelli & Dario Guarascio & Stefan Jestl & Robert Stehrer, 2023. "State Aid, R&D, and the Digital Content of Trade," wiiw Working Papers 237, The Vienna Institute for International Economic Studies, wiiw.
    17. Kenza El Qaoumi & Pascal Le Masson & Benoit Weil & Aytunç Ün, 2017. "Testing Evolutionary Theory of Household Consumption Behavior in the case of Novelty – Product characteristics approach," Post-Print hal-01619967, HAL.
    18. Piotr Tomasz Makowski & Yuya Kajikawa, 2021. "Automation-driven innovation management? Toward Innovation-Automation-Strategy cycle," Papers 2103.02395, arXiv.org.
    19. Barreto, Tais S. & Lanivich, Stephen E. & Cox, Kevin C., 2022. "Temporal orientation as a robust predictor of innovation," Journal of Business Research, Elsevier, vol. 138(C), pages 287-300.
    20. Zheng-Xia He & Shi-Chun Xu & Qin-Bin Li & Bin Zhao, 2018. "Factors That Influence Renewable Energy Technological Innovation in China: A Dynamic Panel Approach," Sustainability, MDPI, vol. 10(1), pages 1-30, January.

    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:jsusta:v:8:y:2016:i:11:p:1211-:d:83579. 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.