Good managers, not machines, drive productivity growth – John Van Reenen

MIT Sloan Professor John Van Reenen

From Bloomberg View

When people discuss what drives long-run productivity, they usually focus on technical change. But productivity is about more than robots, new drugs and self-driving vehicles. First, if you break down the sources of productivity across nations and firms there is a large residual left over (rather inelegantly named “Total Factor Productivity” or TFP for short). And observable measures of technology can only account for a small fraction of this dark matter.

On top of this, a huge number of statistical analyses and case studies of the impact of new technologies on firm performance have shown that there is a massive variation in its impact. What’s much more important than the amount spent on fancy tech is the way managerial practices are used in the firms that implement the changes.

Although there is a tradition in economics starting with the 19th-century American economist Francis Walker on the importance of management for productivity, it has been largely subterranean. Management is very hard to measure in a robust way, so economists have been happy to delegate this task to others in the case study literature in business schools.

Managers are more frequently the butt of jokes from TV shows like “The Office” to “Horrible Bosses,” than seen as drivers of growth. But maybe things are now changing.

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How IoT changes decision making, security and public policy – Erik Brynjolfsson

Professor of Information Technology, Director, The MIT Initiative on the Digital Economy

Professor of Information Technology,
Director, The MIT Initiative on the Digital Economy

We’re in the early stages of a management revolution. The upheaval is based on our unprecedented ability to collect, measure and digitally record information about human and systems activities, particularly with the finely tuned data sets available through IoT. One of the hallmarks of this new era is the acceleration of data-driven decision making within businesses, which has tripled in just five years, according to a recent study I conducted with Kristina McElheren, a professor at University of Toronto.

Accompanying the progress anticipated in this increasingly digital age, however, will be thorny challenges and broader issues for society at large. This is particularly true as organizations begin to feed the large data sets available from IoT into systems that use machine-learning algorithms—at which point they will begin making predictions and decisions in an increasingly automated way, and at large scale.

Machine-learning and artificial intelligence (AI) technologies have advanced greatly in recent years; the implications range much further than the attention they get for winning competitions with “Go” champions and chess masters. The real significance of these technologies will be found in their ability to automate and augment complex decision making.

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Using machine learning to increase meeting efficiency — Cynthia Rudin

MIT Sloan Asst. Prof. Cynthia Rudin

MIT Sloan Asst. Prof. Cynthia Rudin

Meetings play a big role in many people’s jobs. In the U.S. alone, an estimated 11 million meetings take place in a typical day. Managers can spend up to three-quarters of their time in meetings, and approximately 97% of workers say that collaboration is essential to do their best work.

As a result, meetings are tremendously important for businesses. Yet understanding meetings — much less finding ways to increase their productivity — is challenging for researchers because it requires an understanding of many social signals and complex interpersonal dynamics. Most of the work done in this area has been from the social sciences perspective using field work and surveys.

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