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4 Posts authored by: Erik Brynjolfsson

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.


I had a chance to present my research with Joohee Oh about the value of free goods at the recent Techonomy conference in Tuscon. We now to have the full video and transcript available to read and view. As I wrote about here and as Andy McAfee described, I’m convinced that we are in the midst of a major technological revolution that is not being fully reflected in official government statistics, most notably, the GDP and productivity numbers

 

According to official government data, there has bee zero growth in the information sector. But we have all seen an explosion of new digital products and services, from Wikipedia and Facebook, to Youtube and GPS mapping. The official data say this sector is the same size as what it was in the 1960s! Now how can that be? Obviously, there’s some major measurement problems in the way we keep our statistics, and that’s a real concern because, as the saying goes, you can’t manage what you don’t measure. So we need to come up with a better way of measuring things.  That’s what we’ve been working at the MIT Center for Digital Business.

 

Here are a few highlights from my talk:

 

      • We start with the fact that many digital goods are delivered for free. In some cases, they're funded through advertising. In many other cases, users just contribute their time; they develop content and make it available. And maybe there’s a little bit of contributions or advertising that pays for the bandwidth, but those costs are relatively minimal. We know that there’s just been an explosion of the availability of these goods because you can count the number of bits produced; you can count the number of Wikipedia articles produced, for example--and those have grown ten-fold since 2004.
      • Counting bits is a start, but what we really want to know is not just the number of bits but the actual value of information goods. This is where the bug in the GDP measurement occurs; because GDP measures only the total amount spent on goods and services, not their value. So what happens if the price is zero? Well, zero times any quantity is still zero. So you could have an enormous of explosion of bits or articles or whatever else and the statisticians still see it as a big fat zero contribution for our GDP
      • Traditional metrics are really missing what’s going on in this information economy because so much of the digital economy is a free economy. We found a number of other ways to go about measuring it. One is to look at the time that people spent, and that is something that we do. If you just look at the dollars, you’re going to get a sense that actually the economy is stagnant or even shrinking. But we calculate the demand curve for information goods based on the time spent. This is a very different approach than the traditional GDP accounting, but it’s one that we think better captures the real value that goods and services are producing in the economy.
      • After you do the math and plug in the numbers, we estimate that the annual welfare gain from all these free goods is about $300 billion. Now, that’s the average over the past ten years or so. And that works out to about $1,400 per person.

 

We also came up with a way to calibrate the value of this time which can you hear more about if you watch the full video. I will continue to write about our findings as the research unfolds.

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.


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That the amount of business data is skyrocketing is hardly news. All we have to do is consider the huge volumes of data and archives at any major financial institution, retail business or healthcare organization. Then multiply those amounts by a several times and you’ll have an idea of the staggering amount of information amassed at web-based businesses such as Google and Amazon.

 

More important than the quantity of information generated, however, is an understanding of how it is used and how it can create value for organizations, their customers and the overall economy. At the MIT Center for Digital Business, a recent statistical study on the implications of big data offers significant proof that proper use of analytics and business intelligence tools can help businesses use their digital information to grow efficiently and show bottom-line results.

 

Specifically, my paper with Heekyung Kim, Strength in Numbers: How Does Data-Driven Decisionmaking Affect Firm Performance?, finds that “companies that use data-driven analytics instead of intuition have 5%-6% higher productivity and profits than competitors.” Research was based on the business practices and information technology investments of 179 large publicly traded firms. Huge improvements in metrics are allowing a granular analysis of data—whether it resides on mobile devices, in email or elsewhere in data centers-- to find out more about customer behavior. What’s more, the relationship between data-driven decisionmaking and performance also appears in other measures such as asset utilization, return on equity and market value. Our results provide some of the first large-scale data on the direct connection between data-driven decision making and firm performance.

 

As we wrote in the recent Atlantic magazine article:

“Today, businesses can measure their activities and customer relationships with unprecedented precision. As a result, they are awash with data. This is particularly evident in the digital economy, where clickstream data give precisely targeted and real-time insights into consumer behavior.”

 

And while web-based digital companies – notably, Amazon and Google--are in the forefront of data analysis, now we are seeing offline companies in logistics, manufacturing, retail, casinos and finance making use of these techniques as well. Gallo Wines, UPS, Caesar’s Entertainment and Match.com are a few examples cited in the Atlantic article. Marketing and sales organization are leading the way and are far ahead of some other departmental users, but increasingly, we see business units such as HR using email to help internal staff productivity benefits. Similarly, manufacturing lines are gaining access to real-time data from CRM and ERP systems to help them track trends and demand.

 

To delve into this topic in-depth, we are offering a new two-day executive education course on March 28 at MIT Sloan in Cambridge, Mass. [see related blog for details here.]

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