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Big Data e disrupções nos modelos de negócios

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  • 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
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    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.
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