IDEAS home Printed from https://ideas.repec.org/a/fgv/eaerae/v59y2019i6a80776.html
   My bibliography  Save this article

Big Data e disrupções nos modelos de negócios

Author

Listed:
  • van Heck, Eric

Abstract

There are many challenges to reaping the benefits of the newest, emerging technologies. If it were easy, every business would do it and competitive advantage would easily fade away. It is in fact extremely difficult and challenging for companies to create value with emerging technologies. Every year, Gartner, a respected consultancy company, reviews the newest, emerging technologies and uses its hype cycle concept to explain the path that technologies take. The cycle consists of five phases: (i) the technology trigger phase: the invention of new technology that happens in a research lab, usually at a university (most companies outsource their fundamental research to universities nowadays). (ii) the peak of inflated expectations phase: the technology is discussed by companies at conferences and in the press. There is a great deal of talk about the new technology, but no one has used it yet. R&D projects are launched. (iii) the trough of disillusionment phase: it turns out that the technology is not as useful as it was thought to be. (iv) the slope of enlightenment phase: here, the valuable fusion of business and technology is explored. (v) the plateau of productivity phase: it is clear how business can use the technology to create value. In Gartner’s (2019) emerging technologies hype cycle, technologies such as biorobots, augmented reality cloud, decentralized web, adaptive machine learning, nanoscale 3D printing, and 5G are reviewed and positioned in the first phases. Gartner expect that these technologies will reach the plateau of productivity within 5 to 10 years.

Suggested Citation

  • van Heck, Eric, 2019. "Big Data e disrupções nos modelos de negócios," RAE - Revista de Administração de Empresas, FGV-EAESP Escola de Administração de Empresas de São Paulo (Brazil), vol. 59(6), December.
  • Handle: RePEc:fgv:eaerae:v:59:y:2019:i:6:a:80776
    as

    Download full text from publisher

    File URL: http://bibliotecadigital.fgv.br/ojs/index.php/rae/article/view/80776
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Micha T. Kahlen & Wolfgang Ketter & Jan van Dalen, 2018. "Electric Vehicle Virtual Power Plant Dilemma: Grid Balancing Versus Customer Mobility," Production and Operations Management, Production and Operations Management Society, vol. 27(11), pages 2054-2070, November.
    2. Niels Agatz & Ann Campbell & Moritz Fleischmann & Martin Savelsbergh, 2011. "Time Slot Management in Attended Home Delivery," Transportation Science, INFORMS, vol. 45(3), pages 435-449, August.
    3. Yixin Lu & Alok Gupta & Wolfgang Ketter & Eric van Heck, 2019. "Dynamic Decision Making in Sequential Business-to-Business Auctions: A Structural Econometric Approach," Management Science, INFORMS, vol. 65(8), pages 3853-3876, August.
    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. Jasper Grashuis & Theodoros Skevas & Michelle S. Segovia, 2020. "Grocery Shopping Preferences during the COVID-19 Pandemic," Sustainability, MDPI, vol. 12(13), pages 1-10, July.
    2. Fridgen, Gilbert & Keller, Robert & Körner, Marc-Fabian & Schöpf, Michael, 2020. "A holistic view on sector coupling," Energy Policy, Elsevier, vol. 147(C).
    3. Haider, Zulqarnain & Hu, Yujie & Charkhgard, Hadi & Himmelgreen, David & Kwon, Changhyun, 2022. "Creating grocery delivery hubs for food deserts at local convenience stores via spatial and temporal consolidation," Socio-Economic Planning Sciences, Elsevier, vol. 82(PB).
    4. Oliver Thomas & Simon Hagen & Ulrich Frank & Jan Recker & Lauri Wessel & Friedemann Kammler & Novica Zarvic & Ingo Timm, 2020. "Global Crises and the Role of BISE," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 62(4), pages 385-396, August.
    5. Christian Otter & Christian Watzl & Daniel Schwarz & Pamela Priess, 2017. "Towards sustainable logistics: study of alternative delivery facets," Post-Print hal-01861042, HAL.
    6. Subramanyam, Anirudh & Wang, Akang & Gounaris, Chrysanthos E., 2018. "A scenario decomposition algorithm for strategic time window assignment vehicle routing problems," Transportation Research Part B: Methodological, Elsevier, vol. 117(PA), pages 296-317.
    7. Dumez, Dorian & Lehuédé, Fabien & Péton, Olivier, 2021. "A large neighborhood search approach to the vehicle routing problem with delivery options," Transportation Research Part B: Methodological, Elsevier, vol. 144(C), pages 103-132.
    8. Sobrie, Léon & Verschelde, Marijn & Hennebel, Veerle & Roets, Bart, 2023. "Capturing complexity over space and time via deep learning: An application to real-time delay prediction in railways," European Journal of Operational Research, Elsevier, vol. 310(3), pages 1201-1217.
    9. Ehmke, Jan Fabian & Campbell, Ann Melissa, 2014. "Customer acceptance mechanisms for home deliveries in metropolitan areas," European Journal of Operational Research, Elsevier, vol. 233(1), pages 193-207.
    10. Özarık, Sami Serkan & Lurkin, Virginie & Veelenturf, Lucas P. & Van Woensel, Tom & Laporte, Gilbert, 2023. "An Adaptive Large Neighborhood Search heuristic for last-mile deliveries under stochastic customer availability and multiple visits," Transportation Research Part B: Methodological, Elsevier, vol. 170(C), pages 194-220.
    11. Koch, Sebastian & Klein, Robert, 2020. "Route-based approximate dynamic programming for dynamic pricing in attended home delivery," European Journal of Operational Research, Elsevier, vol. 287(2), pages 633-652.
    12. Marco Melacini & Elena Tappia, 2018. "A Critical Comparison of Alternative Distribution Configurations in Omni-Channel Retailing in Terms of Cost and Greenhouse Gas Emissions," Sustainability, MDPI, vol. 10(2), pages 1-15, January.
    13. Agatz, N.A.H. & Campbell, A.M. & Fleischmann, M. & van Nunen, J.A.E.E. & Savelsbergh, M.W.P., 2008. "Demand Management Opportunities in E-fulfillment: What Internet Retailers Can Learn from Revenue Management," ERIM Report Series Research in Management ERS-2008-021-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    14. Remy Spliet & Adriana F. Gabor, 2015. "The Time Window Assignment Vehicle Routing Problem," Transportation Science, INFORMS, vol. 49(4), pages 721-731, November.
    15. Maaike Hoogeboom & Yossiri Adulyasak & Wout Dullaert & Patrick Jaillet, 2021. "The Robust Vehicle Routing Problem with Time Window Assignments," Transportation Science, INFORMS, vol. 55(2), pages 395-413, March.
    16. Strauss, Arne & Gülpınar, Nalan & Zheng, Yijun, 2021. "Dynamic pricing of flexible time slots for attended home delivery," European Journal of Operational Research, Elsevier, vol. 294(3), pages 1022-1041.
    17. Fleckenstein, David & Klein, Robert & Steinhardt, Claudius, 2023. "Recent advances in integrating demand management and vehicle routing: A methodological review," European Journal of Operational Research, Elsevier, vol. 306(2), pages 499-518.
    18. Jose Esteves & Daniel Alonso-Martínez & Guillermo de Haro, 2021. "Profiling Spanish Prospective Buyers of Electric Vehicles Based on Demographics," Sustainability, MDPI, vol. 13(16), pages 1-22, August.
    19. Marlin W. Ulmer & Barrett W. Thomas, 2019. "Enough Waiting for the Cable Guy—Estimating Arrival Times for Service Vehicle Routing," Transportation Science, INFORMS, vol. 53(3), pages 897-916, May.
    20. Shenle Pan & Vaggelis Giannikas & Yufei Han & Etta Grover-Silva & Bin Qiao, 2017. "Using Customer-related Data to Enhance E-grocery Home Delivery," Post-Print hal-01482901, HAL.

    More about this item

    Statistics

    Access and download statistics

    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:fgv:eaerae:v:59:y:2019:i:6:a:80776. 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: Núcleo de Computação da FGV EPGE (email available below). General contact details of provider: https://edirc.repec.org/data/eagvfbr.html .

    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.