The Empirical Economics of Online Attention

Andre  Boik; Shane  Greenstein; Jeffrey  Prince

Working Paper | 2016

The Empirical Economics of Online Attention

by Andre Boik, Shane Greenstein and Jeffrey Prince

Abstract

In several markets, firms compete not for consumer expenditure but instead for consumer attention. We model and characterize how households allocate their scarce attention in arguably the largest market for attention: the Internet. Our characterization of household attention allocation operates along three dimensions: how much attention is allocated, where that attention is allocated, and how that attention is allocated. Using click-stream data for thousands of U.S. households, we assess if and how attention allocation on each dimension changed between 2008 and 2013, a time of large increases in online offerings. We identify vast and expected changes in where households allocate their attention (away from chat and news towards video and social media), and yet we simultaneously identify remarkable stability in how much attention is allocated and how it is allocated. Specifically, we identify (i) persistence in the elasticity of attention according to income and (ii) complete stability in the dispersion of attention across sites and in the intensity of attention within sites. We illustrate how this finding is difficult to reconcile with standard models of optimal attention allocation and suggest alternatives that may be more suitable. We conclude that increasingly valuable offerings change where households go online, but not their general online attention patterns. This conclusion has important implications for competition and welfare in other markets for attention.

Keywords: Online Technology; Competition; Behavior; Resource Allocation; Household; Cognition and Thinking;

Language: English

Format: Print

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Citation:

Boik, Andre, Shane Greenstein, and Jeffrey Prince. "The Empirical Economics of Online Attention." NBER Working Paper Series, No. 22427, July 2016.

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 Empirical Economics of Online Attention

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