The Reference Wars: Encyclopædia Britannica's Decline and Encarta's Emergence

Shane  Greenstein

Article | Strategic Management Journal | May 2017

The Reference Wars: Encyclopædia Britannica's Decline and Encarta's Emergence

Abstract

The experience of Encyclopædia Britannica provides the canonical example of the decline of an established firm at the outset of the digital age. Competition from Microsoft’s Encarta in 1993 led to sharp declines in the sales of books, which led to the distressed sale of the firm in 1996. This essay offers new source material about the actions at both Encarta and Britannica, and it offers a novel interpretation of events. Britannica’s management did not misperceive the opportunities and threats, and Britannica did not lack technical prowess. This narrative stresses that Britannica’s management faced organizational diseconomies of scope between supporting lines of business in the old and new markets, which generated internal conflicts. These conflicts hindered the commercialization of new technology and hastened its decline.

Keywords: digital; Britannica; Software; Books; Publishing Industry;

Format: Print

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Greenstein, Shane. "The Reference Wars: Encyclopædia Britannica's Decline and Encarta's Emergence." Strategic Management Journal 38, no. 5 (May 2017): 995–1017.

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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The study assembles new data to construct a census of worldwide web server use across the globe. We document a large concentration of investment in the United States, and a wide dispersion across scores of countries. We find tens of billions of dollars of unmeasured value in the open source servers. The statistical models show the quality of the country’s network and the country’s technical sophistication are associated with more web servers, and the innovative environment also plays a role. We find less of a role for economic development, property rights and the rule of law. The findings stress that policies for local supply of content depend on policies encouraging advanced networking and a technically sophisticated populace. While the findings highlight the danger for misattribution in growth accounting, the statistical model points towards the potential to proxy for unmeasured servers with statistical methods.

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The Reference Wars: Encyclopædia Britannica's Decline and Encarta's Emergence

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