managers like to think. Instead, what occurs is decision-based evidence making — sometimes without
managers even understanding that it’s happening. The authors address three key questions: Why does
decision-based evidence making occur in organizations? Is decision-based evidence making necessarily
bad? And, if decision-based evidence making is inevitable in organizations, what can be done to lessen
its negative impacts?
To help answer those questions, the authors explain how decision making is affected by the contexts
in which problems are presented — and how those contexts can demand different ways of using evidence, depending on whether the evidence is being used to make, inform or support a decision.
Reprint 51419. To order reprints of this article, see page 10.
Putting the Science in Management Science
Andrew McAfee (Massachusetts Institute of Technology), interviewed by Michael S. Hopkins
pp. 77-82
In an interview that is part of SMR’s new series examining “The New Intelligent Enterprise,” MIT’s
Andrew McAfee, author of Enterprise 2.0, explores how evolving technology and the data deluge can
enable companies not only to be smarter, but to act smarter, too. McAfee focuses special attention on the
needed development by organizations of what he calls a scientific mind-set. Consider the consequences,
he says, of ongoing rapid increases in computing power, storage capacity, communications speed and
instrumentation (sensor technology that enables data capture) — all of which add up to not only a data
deluge but a deluge of new opportunities to parse what information means and how to act on it.
“One of the single biggest changes that I see coming,” says McAfee, “is that when you have this unbelievable amount of horsepower and a mass of data to apply it to, you can be a lot more scientific about
things. You can be a lot more rigorous in your analysis. You can generate and test hypotheses. You can
run experiments. You can adopt a much more scientific mind-set.” Companies that don’t migrate in that
direction, McAfee adds, had better hope that their competitors aren’t heading there, either. “Because
when you compare scientific to pre-scientific approaches, there’s one clear winner over and over.”
Reprint 51414. To order reprints of this article, see page 10.
Best Practices for Industry-University Collaboration
Julio A. Pertuzé (MIT), Edward S. Calder (Innosight), Edward M. Greitzer (MIT), and William A. Lucas
(Bernard M. Gordon-MIT) pp. 83-90
To enhance the capture of new technical knowledge, many companies are engaging in strategic collaborations with universities. But while such partnerships often produce interesting research results — an
insightful technical paper, a proposed process or a new computer code, for example — these outcomes
have little or no business impact. In fact, the authors report, only 40% of the projects in the collaborations that they studied led to an observable and generally agreed upon positive effect on the participating company’s competitiveness or productivity. The other 60% of the projects underachieved, at least
from a business standpoint: The outcomes did not make their way into products or processes, or influence company decisions.
The authors report the findings of a study to identify management practices for industry-university
collaboration that can improve the impact of university research for a company. The likelihood of successful transformation is greatest, they argue, in companies that have specific procedures to create value
from ideas generated by universities. Based on their study, they derive seven “best practices” that will
maximize the chances that collaborating with a university on a research project will produce tangible
benefits for the company. They also point out several factors that companies tend to believe will increase
impact, but that in fact do not.
