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.
Doug Criscitello, Executive Director of MIT’s Center for Finance and Policy
From MIT Golub Center for Finance and Policy
As the keynote speaker at a recent conference of the International Consortium on Government Financial Management held in Washington DC, I had the opportunity to discuss with representatives from over 40 countries one of the primary challenges facing governments around the world – citizen engagement.
My remarks emphasized that recent populist movements should be a wake up call to everyone involved in government – including those in the budgeting and finance communities – on the need to turn citizen cynicism into engagement and buy-in.
The growing availability of technology and data should be enabling a highly informed citizenry (i.e., voters) armed with actionable information. Moving beyond tired factory-like mindsets where government financial staff spend their days grinding out reports, preparing audit remediation plans and manually executing budgets, a modern approach enables technology to drive iterative, customer-focused engagement and creates and marshals electronic resources.
Paul Michelman, editor-in-chief of MIT Sloan Management Review
Within the next five years, how will technology change the practice of management in a way we have not yet witnessed?
MIT Sloan Management Review posed this question to 15 of the world’s foremost experts on the intersection of technology and management who responded in a series of essays available in MIT SMR’s new Fall issue, published online today. The essays were commissioned to celebrate the launch of the magazine’s new Frontiers initiative. Appearing as part of both the print and digital editions, Frontiers explores how technology is reshaping the practice of management.
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.
Many words can come to mind: new, exciting, experimental, small, lean, agile, fast. To me, “startup” mostly makes me think of “agile” and “fast.”
In an early stage startup, everybody is focused on the same thing. People are passionate, enthusiastic, hungry for an opportunity to change the world, and they will do whatever it takes to get things done. At a headcount of 5-10 people, coordination comes naturally. There are no legacy processes to slow things down. Without existing customers, the team is free to modify their products and services as they learn more. There is also a shared sense of urgency. So they run fast: because it’s fun, because they can, and because they have to.