Information Technology and the Distribution of Inventive Activity

Chris  Forman; Avi  Goldfarb; Shane  Greenstein

Working Paper | 2014

Information Technology and the Distribution of Inventive Activity

by Chris Forman, Avi Goldfarb and Shane Greenstein

Abstract

We examine the relationship between the diffusion of advanced internet technology and the geographic concentration of invention, as measured by patents. First, we show that patenting became more concentrated from the early 1990s to the early 2000s and, similarly, that counties that were leaders in patenting in the early 1990s produced relatively more patents by the early 2000s. Second, we compare the extent of invention in counties that were leaders in Internet adoption to those that were not. We see little difference in the growth rate of patenting between leaders and laggards in Internet adoption, on average. However, we find that the rate of patent growth was faster among counties that were not leaders in patenting in the early 1990s but were leaders in Internet adoption by 2000, suggesting that the Internet helped stem the trend towards more geographic concentration. We show that these results are largely driven by patents filed by distant collaborators rather than non-collaborative patents or patents by non-distant collaborators, suggesting low cost long-distance digital communication as a potential mechanism.

Keywords: Patents; Internet; Technology Adoption; Innovation and Invention;

Language: English

Format: Print

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Citation:

Forman, Chris, Avi Goldfarb, and Shane Greenstein. "Information Technology and the Distribution of Inventive Activity." NBER Working Paper Series, No. 20036, April 2014.

Teaching note is meant to accompany Zebra Medical Vision case, which offers a look at a company’s decisions as a small startup competing with other startups and major technology companies. It also demonstrates the challenges faced by a machine learning company working on brand-new technology, including difficulties finding training data, hiring competition, and negotiating FDA approvals and other government regulations. Teaching Note for HBS No. 619-014.

An Israeli startup founded in 2014, Zebra Medical Vision developed algorithms that produced diagnoses from X-rays, mammograms, and CT-scans. The algorithms used deep learning and digitized radiology scans to create software that could assist doctors in making diagnoses. By July 2018, Zebra had developed seven algorithms to analyze scans for emphysema, liver density, compression fractures, bone density, brain bleeds, breast cancer, and a calcium score used to detect calcified plaque in coronary arteries. For each scan analyzed, Zebra charged hospitals $1. By 2018 Zebra found itself in a race with its competitors to perfect these algorithms, create software tools, distribute the tools to physician partners, and create a market. Zebra already had several partners in the U.S. and Europe who gave feedback on its development. Management had to answer the following questions: What should they do next at Zebra? Should they work on the accuracy of the already developed algorithms or continue to develop many new tools? If they chose to develop new products, which applications should they address? And how should they go to market?

Article | MIS Quarterly | September 2018

Do Experts or Crowd-Based Models Produce More Bias? Evidence from Encyclopædia Britannica and Wikipedia

Shane Greenstein and Feng Zhu

Organizations today can use both crowds and experts to produce knowledge. While prior work compares the accuracy of crowd-produced and expert-produced knowledge, we compare bias in these two models in the context of contested knowledge, which involves subjective, unverifiable, or controversial information. Using data from Encyclopædia Britannica, authored by experts, and Wikipedia, an encyclopedia produced by an online community, we compare the slant and bias of pairs of articles on identical topics of U.S. politics. Our slant measure is less (more) than zero when an article leans towards Democratic (Republican) viewpoints, while bias is the absolute value of the slant. We find that Wikipedia articles are more slanted towards Democratic views than are Britannica articles, as well as more biased. The difference in bias between a pair of articles decreases with more revisions. The bias on a per word basis hardly differs between the sources because Wikipedia articles tend to be longer than Britannica articles. These results highlight the pros and cons of each knowledge production model, help identify the scope of the empirical generalization of prior studies comparing the information quality of the two production models, and offer implications for organizations managing crowd-based knowledge production.

Keywords: online community; Collective Intelligence; wisdom of crowds; bias; Wikipedia; Britannica; knowledge production; Knowledge Sharing; Knowledge Dissemination; Prejudice and Bias;

Citation:

Greenstein, Shane, and Feng Zhu. "Do Experts or Crowd-Based Models Produce More Bias? Evidence from Encyclopædia Britannica and Wikipedia." MIS Quarterly 42, no. 3 (September 2018): 945–959. View Details

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Information Technology and the Distribution of Inventive Activity

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