User-Generated Content and Social Media

Michael  Luca

Chapter | Handbook of Media Economics | 2016

User-Generated Content and Social Media

Abstract

This paper documents what economists have learned about user-generated content (UGC) and social media. A growing body of evidence suggests that UGC on platforms ranging from Yelp to Facebook has a large causal impact on economic and social outcomes ranging from restaurant decisions to voting behavior. These findings often leverage unique data sets and methods ranging from regression discontinuity to field experiments, and researchers often work directly with the companies they study. I then survey the factors that influence the quality of UGC. Quality is influenced by factors including promotional content, peer effects between contributors, biases of contributors, and self-selection into the decision to contribute. Nonpecuniary incentives, such as “badges” and social status on a platform, are often used to encourage and steer contributions. I then discuss other issues including business models, network effects, and privacy. Throughout the paper, I discuss open questions in this area.

Keywords: user-generated content; social media; crowdsourcing; design economics; Internet; Marketing; Economics; Media;

Format: Print

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Citation:

Luca, Michael. "User-Generated Content and Social Media." Chap. 12 in Handbook of Media Economics. Vol. 1B, edited by Simon Anderson, Joel Waldfogel, and David Strömberg. North-Holland Publishing Company, 2016.

Working Paper | HBS Working Paper Series | 2018

Product Quality and Entering Through Tying: Experimental Evidence

Hyunjin Kim and Michael Luca

Dominant platform businesses often develop products in adjacent markets to complement their core business. One common approach used to gain traction in these adjacent markets has been to pursue a tying strategy. For example, Microsoft pre-installed Internet Explorer into Windows, and Apple set Apple Maps as the iOS default. Policymakers have raised concerns that dominant platforms may be leveraging their market power to gain traction for lower quality products when they use a tying strategy. In this paper, we empirically explore this question by examining Google’s decision to tie its new reviews product to its search engine. We experimentally vary the reviews displayed above Google’s organic search results to show either exclusively Google reviews (Google’s current tying strategy) or reviews from multiple platforms determined to be the best-performing by Google’s own organic search algorithm. We find that users prefer the version that does not exclude competitor reviews. Furthermore, looking at observational data on user traffic to Yelp from search engines, we find that Google’s exclusion of downstream competitors may have been effective. The share of Yelp’s traffic coming from Google has declined over this period, relative to traffic from Bing and Yahoo (which do not exclude other companies’ reviews), and Google reviews has grown quicker than Yelp and TripAdvisor during the period in which they excluded these (and other) reviews providers. Overall, these results shed light on platform strategy: tying has the potential to facilitate entry in complementary markets, even when the tied product is of worse quality than competitors.

Keywords: Market Entry and Exit; Market Platforms; Competitive Strategy; Product Marketing; Quality;

Citation:

Kim, Hyunjin, and Michael Luca. "Product Quality and Entering Through Tying: Experimental Evidence." Harvard Business School Working Paper, No. 19-045, October 2018. (Revised December 2018. Forthcoming in Management Science.) View Details

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As technology platforms have created new markets and new ways of acquiring information, economists have come to play an increasingly central role in tech companies—tackling problems such as platform design, strategy, pricing, and policy. Over the past five years, hundreds of PhD economists have accepted positions in the technology sector. In this paper, we explore the skills that PhD economists apply in tech companies, the companies that hire them, the types of problems that economists are currently working on, and the areas of academic research that have emerged in relation to these problems.

Because consumer reviews leverage the wisdom of the crowd, the way in which they are aggregated is a central decision faced by platforms. We explore this "rating aggregation problem" and offer a structural approach to solving it, allowing for (1) reviewers to vary in stringency and accuracy, (2) reviewers to be influenced by existing reviews, and (3) product quality to change over time. Applying this to restaurant reviews from Yelp.com, we construct an adjusted average rating and show that even a simple algorithm can lead to large information efficiency gains relative to the arithmetic average.

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User-Generated Content and Social Media

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