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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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There are many metrics and data available that quantify the use of digital goods and services. We know, for example, how many billions songs are downloaded and how much revenue that garners. We can tell how many articles there are on Wikipedia and how many hours people spend on Facebook.


To date, there are primarily three ways to quantify the impact digitization is having: We can look at the contribution of the transactions to the GDP; we can look at an IT company’s stock values, or we can track consumer spending and prices as indicators of consumer surplus created.



But none of these approaches measure the real value of digital services when the services are free, according to our latest research at MIT’s Center for Digital Business. For example, a money-only model may be missing 95% of the value consumers derive online. Finding that new metric is the focus of my team’s latest research. Free goods and services on the Internet have exploded in the past decade, and the average American now spends more than 32 hours a month online.


Once they have an Internet connection, they don't spend money to use Wikipedia, Facebook or Youtube, so the value of these services isn't properly reflected in the GDP statistics.

This gap is a problem we have been grappling with for some time, including in a Sloan Management Review article a few years ago.


At the recent annual meeting we offered a possible solution to the measurement problem: Consumers pay with time, not just money, and where they choose to spend their limited time and attention online is a form of voting. Increasingly, the digital economy is the 'attention economy.' Our research is calculating a demand curve for time and estimating how much consumers implicitly value free goods based on use of their time, not on dollars, spent on the Internet.


One preliminary finding shows that free goods added the equivalent of $139 billion in value to the economy in 2010-- more than 1% of the GDP and equal to $647 per person. The findings may have an impact of calculations such as the GDP and other economic metrics.


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.)



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.

MIT Sloan Professor Scott Stern’s latest research draws a clear correlation between the elements present at the founding of entrepreneurial startups and their later success. In addition, he and MIT doctoral candidate Jorge Guzman, use other widely available data-- such as incorporation information, patents, trademarks, IPOs and venture capital funding-- to measure and identify the potential for future growth.


The findings of the study, “Nowcasting and Placecasting Growth Entrepreneurship,” were presented at an MIT IDE seminar in March by Stern, who is Professor of Management of Technology and Chair of the Technological Innovation, Entrepreneurship and Strategic Management Group at the MIT Sloan School of Management. He and Guzman were not only able to draw conclusions, but to observe data-documented entrepreneurial trends, using algorithms and estimation models. These can “help us understand the origins and dynamics of startups,” Stern said.


Shifting Growth Patterns

Placecasting can be used to “evaluate the role of regional ecosystems” in the growth—and decline -- of startups, and to identify clusters of “hyperinnovation.” “Our approach allows us to track the changing locational patterns of growth entrepreneurs over time,” and in real-time, he said, as opposed to traditional, static survey methods. For example, “in Massachusetts, we are able to document the transition from Route 128 growth entrepreneurship to clustering in Kendall Square in Cambridge and Boston.” Similarly, in California, he is tracking the move of entrepreneurship from Silicon Valley to San Francisco.

guzman chart.JPG

Using what he calls nowcasting, Stern expects to develop a predictive model of growth outcomes and assign a probability of growth based on current developments and past indicators. It will also be easier to spot and evaluate why some firms will not succeed. Going forward, Stern also understands that the pace of change and the “app economy” will require new criteria and there will be new shifts to track.


Stern works widely with both companies and governments in understanding the drivers and consequences of innovation and entrepreneurship, and has worked extensively in understanding the role of innovation and entrepreneurship in competitiveness and regional economic performance. For more about regional clusters, watch this video and for more on the research, contact Stern at .