Implications of the ICT Revolution for Business

  • Nagy K. Hanna
Part of the Innovation, Technology, and Knowledge Management book series (ITKM)


In this chapter, I explore the many ways ICT is likely to impact competitiveness, private sector development and business innovation. The discussion is future-oriented, as the impact of the ICT revolution will become most evident and pervasive only in later stages of diffusion, consolidation and transformation. But by then, windows of opportunities would be lost and competitive positions of many developing countries would be significantly eroded. Most evidence therefore comes from OECD countries, or is slowly emerging from a few advanced developing countries. Some of the evidence is limited to pioneering or first-in-class companies, and thus detects early signs or emerging trends rather than common practice. On the basis of this evidence, I believe that ICT offers many promises and opportunities, even while posing serious risks and uncertainties for businesses in developing countries. Its impact is likely to be pervasive across all industries and services.

First, I explore some of the promises and manifestations of the ICT revolution. Next, I suggest that we are still in an early phase of a long-term technological wave and productivity revolution. Promising paradigm shifts within computing and communication suggest continuing dramatic decline in prices and increase in performance and intelligence of ICT systems. Moreover, long adjustment periods are needed for an economy to fully benefit from a disruptive new technology. The ICT revolution and the accompanying socio-economic adjustments constitute a techno-economic paradigm shift with profound implications for the renewal of the productive and institutional structures in developed and developing countries alike.


Supply Chain Mobile Phone Total Factor Productivity Business Intelligence Global Supply Chain 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.University of MarylandBethesdaUSA

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