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Why Should We Care About Productivity? An important question that has been debated for almost a decade is whether computers contribute to productivity growth. Productivity isn't everything. However, as noted […]

Information technology managers are facing enormous pressure to take business-process transformation to a new level. Our analysis of survey data gathered for this special issue of InformationWeek indicates that getting […]

Information technology is the defining tool of the emerging information economy. IT promises to create new sources of value and redefine industry boundaries. But it also threatens to absorb millions […]

The article focuses on visionaries and innovators on Newsweek's inaugural list of Greatest Disruptors. Topics discussed include development of affordable human-like bionic limbs by seeing a little girl missing a leg using a tree branch; focus on employing technology in a way that is driving fundamental, transformative change in business, health, the environment, culture or society; and work on teaching computers to understand human emotion.

Successful companies support good business plans with the right information technologies In the past couple of years, U.S. companies have gone on a computer spending binge. But is technology alone […]

The relationship between information technology (IT) and productivity is widely discussed but little understood. Delivered computing power in the U.S. economy has increased by more than two orders of magnitude […]

AbstractGeneral purpose technologies (GPTs) like AI enable and require significant complementary investments. These investments are often intangible and poorly measured in national accounts. We develop a model that shows how this can lead to underestimation of productivity growth in a new GPTs early years and, later, when the benefits of intangible investments are harvested, productivity growth overestimation. We call this phenomenon the Productivity J-curve. We apply our method to US data and find that adjusting for intangibles related to computer hardware and software yields a TFP level that is 15.9 percent higher than official measures by the end of 2017. (JEL E22, E23, G31, L63, L86)

Big data, the authors write, is far more powerful than the analytics of the past. Executives can measure and therefore manage more precisely than ever before. They can make better predictions and smarter decisions. They can target more-effective interventions in areas that so far have been dominated by gut and intuition rather than by data and rigor. The differences between big data and analytics are a matter of volume, velocity, and variety: More data now cross the internet every second than were stored in the entire internet 20 years ago. Nearly real-time information makes it possible for a company to be much more agile than its competitors. And that information can come from social networks, images, sensors, the web, or other unstructured sources. The managerial challenges, however, are very real. Senior decision makers have to learn to ask the right questions and embrace evidence-based decision making. Organizations must hire scientists who can find patterns in very large data sets and translate them into useful business information. IT departments have to work hard to integrate all the relevant internal and external sources of data. The authors offer two success stories to illustrate how companies are using big data: PASSUR Aerospace enables airlines to match their actual and estimated arrival times. Sears Holdings directly analyzes its incoming store data to make promotions much more precise and faster. [ABSTRACT FROM AUTHOR]