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MIT CDB Assistant Professor, Renee Gosline, Describes Digital Marketing's Newest Influencers

MIT CDB Video: Sinan Aral on Social Commerce


At the recent MIT CDB conference on Big Data, professor and social networking expert, Sinan Aral, discusses peer influence and how it can impact product marketing strategies and the online economy.




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I teach at a School of Management so you won’t be surprised to learn that I think good management can make a huge difference in the performance of companies, and ultimately the economy.  But you may be surprised that there is very little economic research on the effects of management.  Sure, there’s lots of speculation and countless management books and articles, but a recent review of the economic literature by Chad Syverson concluded: “No potential driving factor of productivity has seen a higher ratio of speculation to empirical study [than management practices].”  The biggest problem has been simply a lack of a comprehensive, reliable data set of management practices.

 

To address this gap, I recently helped formulate the U.S. Census Bureau’s survey of management and organizational practices at more than 30,000 manufacturing plants across the country--the first large-scale survey of management in America. Along with Nick Bloom, Lucia Foster, Ron Jarmin, Itay Saporta and John Van Reenen, we examined three types of practices-- performance monitoring; setting targets, and offering incentives—which we called “Structured Management.”

 

Analysis of the data reveals several striking results about the relationship between performance goals and improved business. Specifically, setting business goals and monitoring results are among the practices that actually yield better business productivity and growth, according to this comprehensive survey of U.S. management conducted in 2011. The survey was funded by the National Science Foundation and had administrative support from the National Bureau of Economic Research and the MIT Center for Digital Business. It was a joint, academic-U.S. census bureau collaboration.

 

My fellow researchers and I set out to determine whether, and what type of management practices influence bottom-line business results such as productivity, output and growth. Based on the responses, we found a tight link between Structured Management and performance outcomes such as growth, expenditures and innovation as indicated by R&D and patent intensity.

 

Figure 1: Better Performance is Associated With More Structured Management

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While the survey did not focus exclusively on digital technologies, the conclusions may partly reflect the increasing adoption of information technologies, like Enterprise Resource Planning (ERP) systems, which make data collection and processing much cheaper, easier and more effective. Structured Management scores for data use have improved the most, according to the data. Presumably this reflects the growing use of IT in modern firms.

 

The study also highlights the important rise of data-driven decisionmaking, which the MIT CDB has championed for several years. Most of the rise in structured management practices, for example, has come among businesses that have implemented data-driven performance monitoring.

 

Figure 2: Average Management Scores Increased between 2005 and 2010,Especially for Data Driven Performance Monitoring

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It was also interesting to note that adoption of structured management practices has increased between 2005 and 2010, particularly for those practices involving data collection and analysis.  This is consistent with my earlier research with Lorin Hitt and Heekyung Kim on Data-Driven Decisionmaking.

 

Among other key findings:

 

-- There is a substantial dispersion of management practices across the establishments. Eighteen percent have adopted at least 75% of these more structured management practices, while 27% adopted less than 50% of these.

 

--There is a positive correlation between structured management practices and location, firm size, establishment-level measures of worker education, and export status.

 

Going forward we will continue to analyze the data and explore causality. Additionally, we may do another survey in 2015 to establish longer-term data and perhaps will focus on the retail or health-care sector.

 

Let me know what other ideas you think we should explore.


Digital automation, and its impact on labor, society and the economy, has been studied from multiple perspectives and through many lenses.  In his new research and analysis, Daron Acemoglu, the Elizabeth and James Killian Professor of Economics at MIT, acknowledges inequalities created when automation displaces certain human skills. However, he also says it is possible for new technology to create more complex versions of existing tasks where labor has a comparative advantage, tipping the scales back toward a future with plentiful jobs.

http://economics.mit.edu/timages/7


Acemoglu completed his graduate work in mathematical economics and econometrics at the London School of Economics, where he also received his Ph.D. in economics. His recent research focuses on the political, economic and social causes of differences in economic development across societies; the factors affecting the institutional and political evolution of nations; and how technology impacts growth and distribution of resources. Acemoglu has published four books: Economic Origins of Dictatorship and Democracy (joint with James A. Robinson), Introduction to Modern Economic Growth, Why Nations Fail: The Origins of Power, Prosperity, and Poverty (joint with James A. Robinson), and Principles of Economics (joint with David Laibson and John List).


He recently spoke at an MIT IDE seminar on the topic of, "The Race between Machine and Man: Implications of Technology for Growth, Factor Shares and Employment.” IDE Community manager Paula Klein followed up with four questions. Below are his responses.

 

 

Q.  The current rivalry between digital automation and humans seems focused on economics and labor issues— concerns that labor will be progressively marginalized and made redundant by new technologies. Is this focus premature or overstated?


A: It is certainly not premature. We have seen many different types of tasks produced and performed by labor, even fairly skilled labor, become automated over the last 30 years. We also know of new technologies that will automate some very major occupations (regulations permitting), including driving, airplane piloting, some aspects of surgery, certain types of diagnoses and even parts of the practice of law.

Yet there is an aspect of it that is overstated. These are still only some of the occupations that humans perform today. The more important overstatement comes as one turns from automation to the prospects for future employment creation. This rapid process of automation does not mean that the future economy will not create jobs. If you look at the last several decades, qualitative evidence suggests rapid automation has been going on for more than a century, and a lot of the new employment comes in new tasks and occupations. So, as machines take jobs previously performed by humans, the economy appears to create yet other tasks and jobs to employ the displaced workers.

 

Q: How does your task-based framework help explain the current economic situation and provide context? Can you briefly summarize the model and your research?


A: Our framework helps us understand the aforementioned patterns and why the fact that new employment will come from new tasks and activities. But more importantly, because it endogenizes the speeds at which existing tasks are automated and new tasks are created, it also highlights why a period of unusually rapid automation generally brings a subsequent period of rapid creation of new tasks. Put simply, rapid automation depresses the price of labor which has fewer tasks to work. This then makes it more profitable for new tasks, which employ new labor, to be created.

 

Q: How might these new tasks spur economic growth and innovation?


A: The growth implications of creating new tasks are essentially a corollary of what I have just described. Growth comes about both because of automation -- we can do things we have been doing more cheaply-- and because of the creation of new tasks; we have new goods and services using better technology. Anything that spurs innovation triggers faster economic growth. So, rapid automation is a double whammy: it benefits us directly and it spurs additional creation of further growth-enhancing new tasks.

 

Q: What guidance can you offer to employers, workers, students and policymakers to prepare and adjust for the Second Machine Age?


A: All of these scenarios are no consolation if you do not have the skills that new tasks and jobs will demand. Some economists are now questioning whether college is a good investment. There are certainly reasons for rethinking some of our long-cherished assumptions: college is expensive and college graduates have not done very well in the labor force over the last 15 years or so. Nevertheless, improving the skills of our workforce and improving our own skills still remain the only ways of ensuring that we adapt to the future of technology.

When machine “workers” are on 24 x 7 shifts, how can humans compete? When autonomous drones can achieve tasks without human intervention, what are our moral responsibilities?

 

In the rush to bring newer, smarter and more capable technologies to market, few are addressing the ethical and moral dilemmas that automation has raised. Psychology professor Joshua Greene, Director of the Moral Cognition Lab at Harvard University, however, is starting to relate his research about the brain and human morality to the world of IT and robotics.

 

At a February 18 seminar hosted by the MIT IDE, Greene noted that until recently, he didn’t fully make the connection between his own work and the long-term issues of Artificial Intelligence (AI). That intersection becomes very clear, however, when you think about the real-world issues of job displacement, how machines are programmed and what they are instructed to do. (More about Automated Ethics can be found here and here.)

 

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The idea of machine intelligence displacing human labor--as discussed in MIT’s Erik Brynolfsson and Andrew McAfee’s book, The Second Machine Age--is no longer science fiction; “it’s not crazy,” Greene said.

 

Drawing on insights from his 2013 book, Moral Tribes: Emotion, Reason, and the Gap Between Us and Them, Greene explained that we react most strongly to harmful actions like punching someone in the face, where the harm is caused intentionally and directly, and the victim is an identifiable person. The social and moral challenges posed by advancing AI are different. If advanced AI puts millions of people out of work it won’t feel like intentionally punching someone—or a million people. The harm will be caused as an indirect side effect of doing something good. And those affected will be “statistical” people rather than identified individuals. It’s this mismatch between our moral psychology and the consequences at stake that makes modern moral problems so challenging.

 

Greene believes more focus is needed on critical problems like whether--and how—moral sensibilities can be programmed into autonomous machines such as military drones and self-driving cars. On a larger scale, societies have to re-imagine the world as one in which machines do more and more of the work currently done by humans. Technological advances may soon outpace our own moral sensibilities, according to Greene. “We’ll need to find new solutions.”

 

 

 

 

Joshua D. Greene is Professor of Psychology, a member of the Center for Brain Science faculty, and the director of the Moral Cognition Lab at Harvard University. He studies the psychology and neuroscience of morality, focusing on the interplay between emotion and reasoning in moral decision-making. His broader interests cluster around the intersection of philosophy, psychology, and neuroscience. He is the author of Moral Tribes: Emotion, Reason, and the Gap Between Us and Them.