IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v94y2015icp1-20.html
   My bibliography  Save this article

A structured scenario approach to multi-screen ecosystem forecasting in Korean communications market

Author

Listed:
  • Chang, Suk-Gwon

Abstract

Ecosystem forecasting is a challenge for any forecaster since it has a large number of variables, which vary dynamically, tightly coupled with environmental factors under a complex ecosystem architecture. The ecosystem behaves like a complex system as a whole where one variable may serve as a hierarchical pillar to other variables, while others interact with each other in non-linear forms of substitution, complementarity, synergy and externalities. This paper is targeted to develop a profound structured approach to the ecosystem forecasting which combines scenario planning with technological forecasting. Three key planning principles are derived and incorporated into the structured ecosystem forecasting methodology. To demonstrate its effectiveness, the Korean multi-screen service market is analyzed and prospected toward the year 2016. Policy and strategic implications from the structured ecosystem forecasting are also discussed to validate the practicality of the suggested methodology.

Suggested Citation

  • Chang, Suk-Gwon, 2015. "A structured scenario approach to multi-screen ecosystem forecasting in Korean communications market," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 1-20.
    • Handle: RePEc:eee:tefoso:v:94:y:2015:i:c:p:1-20
    DOI: 10.1016/j.techfore.2014.04.005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162514001267
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2014.04.005?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. Battistella, Cinzia & Colucci, Katia & De Toni, Alberto F. & Nonino, Fabio, 2013. "Methodology of business ecosystems network analysis: A case study in Telecom Italia Future Centre," Technological Forecasting and Social Change, Elsevier, vol. 80(6), pages 1194-1210.
    2. Varho, Vilja & Tapio, Petri, 2013. "Combining the qualitative and quantitative with the Q2 scenario technique — The case of transport and climate," Technological Forecasting and Social Change, Elsevier, vol. 80(4), pages 611-630.
    3. Shin, Dong-Hee & Song, Hae-Ryong, 2012. "The switchover to digital broadcasting in Korea," Technological Forecasting and Social Change, Elsevier, vol. 79(8), pages 1447-1461.
    4. Warth, Johannes & von der Gracht, Heiko A. & Darkow, Inga-Lena, 2013. "A dissent-based approach for multi-stakeholder scenario development — The future of electric drive vehicles," Technological Forecasting and Social Change, Elsevier, vol. 80(4), pages 566-583.
    5. Anonymous, 2013. "Introduction to the Issue," Journal of Wine Economics, Cambridge University Press, vol. 8(3), pages 243-243, December.
    6. Philippe Durance & Michel Godet, 2010. "Scenario building: Uses and abuses," Post-Print hal-02864615, HAL.
    7. Veronika Gustafsson & Erich J. Schwarz, 2013. "Business Modelling and Convergence," Springer Books, in: Sandra Diehl & Matthias Karmasin (ed.), Media and Convergence Management, edition 127, chapter 2, pages 9-23, Springer.
    8. Widiarta, Handik & Viswanathan, S. & Piplani, Rajesh, 2009. "Forecasting aggregate demand: An analytical evaluation of top-down versus bottom-up forecasting in a production planning framework," International Journal of Production Economics, Elsevier, vol. 118(1), pages 87-94, March.
    9. Pincombe, Brandon & Blunden, Sarah & Pincombe, Adrian & Dexter, Patricia, 2013. "Ascertaining a hierarchy of dimensions from time-poor experts: Linking tactical vignettes to strategic scenarios," Technological Forecasting and Social Change, Elsevier, vol. 80(4), pages 584-598.
    10. Chatterjee, Kiron & Gordon, Andrew, 2006. "Planning for an unpredictable future: Transport in Great Britain in 2030," Transport Policy, Elsevier, vol. 13(3), pages 254-264, May.
    11. Wilkinson, Angela & Kupers, Roland & Mangalagiu, Diana, 2013. "How plausibility-based scenario practices are grappling with complexity to appreciate and address 21st century challenges," Technological Forecasting and Social Change, Elsevier, vol. 80(4), pages 699-710.
    12. Bowman, Gary & MacKay, R. Bradley & Masrani, Swapnesh & McKiernan, Peter, 2013. "Storytelling and the scenario process: Understanding success and failure," Technological Forecasting and Social Change, Elsevier, vol. 80(4), pages 735-748.
    13. Nigel Meade & Towhidul Islam, 1998. "Technological Forecasting---Model Selection, Model Stability, and Combining Models," Management Science, INFORMS, vol. 44(8), pages 1115-1130, August.
    14. Wright, George & Bradfield, Ron & Cairns, George, 2013. "Does the intuitive logics method – and its recent enhancements – produce “effective” scenarios?," Technological Forecasting and Social Change, Elsevier, vol. 80(4), pages 631-642.
    15. Anonymous, 2013. "Introduction to the Issue," Journal of Wine Economics, Cambridge University Press, vol. 8(2), pages 129-130, November.
    16. von der Gracht, Heiko A. & Stillings, Christopher, 2013. "An innovation-focused scenario process — A case from the materials producing industry," Technological Forecasting and Social Change, Elsevier, vol. 80(4), pages 599-610.
    17. Zotteri, Giulio & Kalchschmidt, Matteo, 2007. "A model for selecting the appropriate level of aggregation in forecasting processes," International Journal of Production Economics, Elsevier, vol. 108(1-2), pages 74-83, July.
    18. Barry R. Cobb, 2007. "Influence Diagrams with Continuous Decision Variables and Non-Gaussian Uncertainties," Decision Analysis, INFORMS, vol. 4(3), pages 136-155, September.
    19. De Smedt, Peter & Borch, Kristian & Fuller, Ted, 2013. "Future scenarios to inspire innovation," Technological Forecasting and Social Change, Elsevier, vol. 80(3), pages 432-443.
    20. Park, Ho-Young & Chang, Suk-Gwon, 2004. "Mobile network evolution toward IMT-2000 in Korea: a techno-economic analysis," Telecommunications Policy, Elsevier, vol. 28(2), pages 177-196, March.
    21. Önkal, Dilek & Sayım, Kadire Zeynep & Gönül, Mustafa Sinan, 2013. "Scenarios as channels of forecast advice," Technological Forecasting and Social Change, Elsevier, vol. 80(4), pages 772-788.
    22. Zotteri, Giulio & Kalchschmidt, Matteo & Caniato, Federico, 2005. "The impact of aggregation level on forecasting performance," International Journal of Production Economics, Elsevier, vol. 93(1), pages 479-491, January.
    23. Ronald A. Howard & James E. Matheson, 2005. "Influence Diagram Retrospective," Decision Analysis, INFORMS, vol. 2(3), pages 144-147, September.
    24. MacKay, Brad & Tambeau, Paul, 2013. "A structuration approach to scenario praxis," Technological Forecasting and Social Change, Elsevier, vol. 80(4), pages 673-686.
    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. Cheng, M.N. & Wong, Jane W.K. & Cheung, C.F. & Leung, K.H., 2016. "A scenario-based roadmapping method for strategic planning and forecasting: A case study in a testing, inspection and certification company," Technological Forecasting and Social Change, Elsevier, vol. 111(C), pages 44-62.
    2. Dedehayir, Ozgur & Mäkinen, Saku J. & Roland Ortt, J., 2018. "Roles during innovation ecosystem genesis: A literature review," Technological Forecasting and Social Change, Elsevier, vol. 136(C), pages 18-29.

    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. Ramboarison-Lalao, Lovanirina & Gannouni, Kais, 2019. "Liberated firm, a leverage of well-being and technological change? A prospective study based on the scenario method," Technological Forecasting and Social Change, Elsevier, vol. 140(C), pages 129-139.
    2. Cairns, George & Wright, George & Fairbrother, Peter, 2016. "Promoting articulated action from diverse stakeholders in response to public policy scenarios: A case analysis of the use of ‘scenario improvisation’ method," Technological Forecasting and Social Change, Elsevier, vol. 103(C), pages 97-108.
    3. Metz, Ashley & Hartley, Paul, 2020. "Scenario development as valuation: Opportunities for reflexivity," Technological Forecasting and Social Change, Elsevier, vol. 155(C).
    4. Phadnis, Shardul & Caplice, Chris & Singh, Mahender & Sheffi, Yossi, 2014. "Axiomatic foundation and a structured process for developing firm-specific Intuitive Logics scenarios," Technological Forecasting and Social Change, Elsevier, vol. 88(C), pages 122-139.
    5. Marasco, A. & Picucci, A. & Romano, A., 2016. "Market share dynamics using Lotka–Volterra models," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 49-62.
    6. Wright, George & Cairns, George & O'Brien, Frances A. & Goodwin, Paul, 2019. "Scenario analysis to support decision making in addressing wicked problems: Pitfalls and potential," European Journal of Operational Research, Elsevier, vol. 278(1), pages 3-19.
    7. Kishita, Yusuke & Mizuno, Yuji & Fukushige, Shinichi & Umeda, Yasushi, 2020. "Scenario structuring methodology for computer-aided scenario design: An application to envisioning sustainable futures," Technological Forecasting and Social Change, Elsevier, vol. 160(C).
    8. Bourgeois, Robin & Penunia, Esther & Bisht, Sonali & Boruk, Don, 2017. "Foresight for all: Co-elaborative scenario building and empowerment," Technological Forecasting and Social Change, Elsevier, vol. 124(C), pages 178-188.
    9. Crawford, Megan M., 2019. "A comprehensive scenario intervention typology," Technological Forecasting and Social Change, Elsevier, vol. 149(C).
    10. Meissner, Philip & Brands, Christian & Wulf, Torsten, 2017. "Quantifiying blind spots and weak signals in executive judgment: A structured integration of expert judgment into the scenario development process," International Journal of Forecasting, Elsevier, vol. 33(1), pages 244-253.
    11. Cairns, George & Wright, George & Fairbrother, Peter & Phillips, Richard, 2017. "‘Branching scenarios’ seeking articulated action for regional regeneration – A case study of limited success," Technological Forecasting and Social Change, Elsevier, vol. 124(C), pages 189-202.
    12. Derbyshire, James, 2017. "Potential surprise theory as a theoretical foundation for scenario planning," Technological Forecasting and Social Change, Elsevier, vol. 124(C), pages 77-87.
    13. Sbrana, Giacomo & Silvestrini, Andrea, 2013. "Forecasting aggregate demand: Analytical comparison of top-down and bottom-up approaches in a multivariate exponential smoothing framework," International Journal of Production Economics, Elsevier, vol. 146(1), pages 185-198.
    14. Ram, Camelia, 2020. "Scenario presentation and scenario generation in multi-criteria assessments: An exploratory study," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    15. Hussain, M. & Tapinos, E. & Knight, L., 2017. "Scenario-driven roadmapping for technology foresight," Technological Forecasting and Social Change, Elsevier, vol. 124(C), pages 160-177.
    16. Babai, Zied & Boylan, John E. & Kolassa, Stephan & Nikolopoulos, Konstantinos, 2016. "Supply chain forecasting: Theory, practice, their gap and the futureAuthor-Name: Syntetos, Aris A," European Journal of Operational Research, Elsevier, vol. 252(1), pages 1-26.
    17. Addolorata Marasco & Alessandro Romano, 2018. "Deterministic modeling in scenario forecasting: estimating the effects of two public policies on intergenerational conflict," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(5), pages 2345-2371, September.
    18. Sali, Mustapha & Ghrab, Yahya & Chatras, Clément, 2023. "Optimal product aggregation for sales and operations planning in mass customisation context," International Journal of Production Economics, Elsevier, vol. 263(C).
    19. Tiberius, Victor & Siglow, Caroline & Sendra-García, Javier, 2020. "Scenarios in business and management: The current stock and research opportunities," Journal of Business Research, Elsevier, vol. 121(C), pages 235-242.
    20. Derbyshire, James & Wright, George, 2017. "Augmenting the intuitive logics scenario planning method for a more comprehensive analysis of causation," International Journal of Forecasting, Elsevier, vol. 33(1), pages 254-266.

    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:94:y:2015:i:c:p:1-20. 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.