Published Papers

This article uses actual word-of-mouth (WOM) information to examine the dynamic patterns of WOM and how it helps explain box office revenue. The WOM data were collected from the Yahoo Movies Web site. The results show that WOM activities are the most active during a movie's prerelease and opening week and that movie audiences tend to hold relatively high expectations before release but become more critical in the opening week. More important, WOM information offers significant explanatory power for both aggregate and weekly box office revenue, especially in the early weeks after a movie opens. However, most of this explanatory power comes from the volume of WOM and not from its valence, as measured by the percentages of positive and negative messages.

Many consumers base their purchase decisions on online consumer reviews. An overlooked feature of these texts is their narrativity: the extent to which they tell a story. The authors construct a new theory of narrativity to link the narrative content and discourse of consumer reviews to consumer behavior. They also develop from scratch a computerized technique that reliably determines the degree of narrativity of 190,461 verbatim, online consumer reviews and validate the automated text analysis with two controlled experiments. More transporting (i.e., engaging) and persuasive reviews have better-developed characters and events as well as more emotionally changing genres and dramatic event orders. This interdisciplinary, multimethod research should help future researchers (1) predict how narrativity affects consumers' narrative transportation and persuasion, (2) measure the narrativity of large digital corpora of textual data, and (3) understand how this important linguistic feature varies along a continuum.

Does the number of people with whom someone communicates influence what he or she discusses and shares? Six studies demonstrate that compared with narrowcasting (i.e., communicating with just one person), broadcasting (i.e., communicating with multiple people) leads consumers to avoid sharing content that makes them look bad. Narrowcasting, however, encourages people to share content that is useful to the message recipient. These effects are driven by communicators' focus of attention. People naturally tend to focus on the self, but communicating with just one person heightens other-focus, which leads communicators to share less self-presenting content and more useful content. These findings shed light on the drivers of word of mouth and provide insight into when the communication sender (vs. receiver) plays a relatively larger role in what people share.

Internet review forums increasingly supplement expert opinion and social networks in informing consumers about product quality. However, limited empirical evidence links digital word-of-mouth to purchasing decisions. We implement a regression discontinuity design to estimate the effect of positive Yelp.com ratings on restaurant reservation availability. An extra half-star rating causes restaurants to sell out 19 percentage points (49%) more frequently, with larger impacts when alternate information is more scarce. These returns suggest that restaurateurs face incentives to leave fake reviews but a rich set of robustness checks confirm that restaurants do not manipulate ratings in a confounding, discontinuous manner.

The authors examine the effect of consumer reviews on relative sales of books at Amazon.com and Barnesandnoble.com. The authors find that (1) reviews are overwhelmingly positive at both sites, but there are more reviews and longer reviews at Amazon.com; (2) an improvement in a book's reviews leads to an increase in relative sales at that site; (3) for most samples in the study, the impact of one-star reviews is greater than the impact of five-star reviews; and (4) evidence from review-length data suggests that customers read review text rather than relying only on summary statistics.

The greater power of bad events over good ones is found in everyday events, major life events (e.g., trauma), close relationship outcomes, social network patterns, interpersonal interactions, and learning processes. Bad emotions, bad parents, and bad feedback have more impact than good ones, and bad information is processed more thoroughly than good. The self is more motivated to avoid bad self-definitions than to pursue good ones. Bad impressions and bad stereotypes are quicker to form and more resistant to disconfirmation than good ones. Various explanations such as diagnosticity and salience help explain some findings, but the greater power of bad events is still found when such variables are controlled. Hardly any exceptions (indicating greater power of good) can be found. Taken together, these findings suggest that bad is stronger than good, as a general principle across a broad range of psychological phenomena.

Peer effects, in which the behavior of an individual is affected by the behavior of their peers, are central to social science. Because peer effects are often confounded with homophily and common external causes, recent work has used randomized experiments to estimate effects of specific peer behaviors. These experiments have often relied on the experimenter being able to randomly modulate mechanisms by which peer behavior is transmitted to a focal individual. We describe experimental designs that instead randomly assign individuals' peers to encouragements to behaviors that directly affect those individuals. We illustrate this method with a large peer encouragement design on Facebook for estimating the effects of receiving feedback from peers on posts shared by focal individuals. We find evidence for substantial effects of receiving marginal feedback on multiple behaviors, including giving feedback to others and continued posting. These findings provide experimental evidence for the role of behaviors directed at specific individuals in the adoption and continued use of communication technologies. In comparison, observational estimates differ substantially, both underestimating and overestimating effects, suggesting that researchers and policy makers should be cautious in relying on them.

User-generated content on social media platforms and product search engines is changing the way consumers shop for goods online. However, current product search engines fail to effectively leverage information created across diverse social media platforms. Moreover, current ranking algorithms in these product search engines tend to induce consumers to focus on one single product characteristic dimension (e.g., price, star rating). This approach largely ignores consumers' multidimensional preferences for products. In this paper, we propose to generate a ranking system that recommends products that provide, on average, the best value for the consumer's money. The key idea is that products that provide a higher surplus should be ranked higher on the screen in response to consumer queries. We use a unique data set of U.S. hotel reservations made over a three-month period through Travelocity, which we supplement with data from various social media sources using techniques from text mining, image classification, social geotagging, human annotations, and geomapping. We propose a random coefficient hybrid structural model, taking into consideration the two sources of consumer heterogeneity the different travel occasions and different hotel characteristics introduce. Based on the estimates from the model, we infer the economic impact of various location and service characteristics of hotels. We then propose a new hotel ranking system based on the average utility gain a consumer receives from staying in a particular hotel. By doing so, we can provide customers with the "best-value" hotels early on. Our user studies, using ranking comparisons from several thousand users, validate the superiority of our ranking system relative to existing systems on several travel search engines. On a broader note, this paper illustrates how social media can be mined and incorporated into a demand estimation model in order to generate a new ranking system in product search engines. We thus highlight the tight linkages between user behavior on social media and search engines. Our interdisciplinary approach provides several insights for using machine learning techniques in economics and marketing research.

Many businesses today have adopted tweeting as a new form of product marketing. However, whether and how tweeting affects product demand remains inconclusive. The authors explore this question using a randomized field experiment on Sina Weibo, the top tweeting website in China. The authors collaborate with a major global media company and examine how the viewing of its TV shows is affected by (1) the media company's tweets about its shows, and (2) recruited Weibo influentials' retweets of the company tweets. The authors find that both company tweets and influential retweets increase show viewing, but in different ways. Company tweets directly boost viewing, whereas influential retweets increase viewing if the show tweet is informative. Meanwhile, influential retweets are more effective than company tweets in bringing new Weibo followers to the company, which indirectly increases viewing. The authors discuss recommendations on how to manage tweeting as a marketing tool.

This paper examines the informational role of product ratings. We build a theoretical model in which ratings can help consumers figure out how much they would enjoy the product. In our model, a high average rating indicates a high product quality, whereas a high variance of ratings is associated with a niche product, one that some consumers love and others hate. Based on its informational role, a higher variance would correspond to a higher subsequent demand if and only if the average rating is low. We find empirical evidence that is consistent with the theoretical predictions with book data from Amazon.com and BN.com. A higher standard deviation of ratings on Amazon improves a book's relative sales rank when the average rating is lower than 4.1 stars, which is true for 35% of all the books in our sample.

This research examines how the positive or negative valence of proprietary information affects both the likelihood that people diffuse this information through their social networks and the likelihood that recipients' access to this information provides them with a source of comparative advantage. Using a unique dataset of over 2 million stock trades and associated profits and losses, and 1 million instant messages exchanged between professional day traders at a U.S. hedge fund, we show that day traders are more likely to talk about their gains than their losses with their close contacts, suggesting that positive information is more likely to be shared among one's close network of strong ties. However, by examining the subsequent behaviors of message recipients, we find that recipients tend to discount the value of positive, gains related information, being both more likely to pass on and profit from negative information related to trading losses, particularly from their strong ties. Our results suggest that although individuals are more likely to share positive information with their contacts, message recipients appear to account for the asymmetry in their subsequent communications and decision-making.

This paper exploits a novel hand-collected data set to provide a comprehensive analysis of the social relationships that underlie illegal insider trading networks. I find that inside information flows through strong social ties based on family, friends, and geographic proximity. On average, inside tips originate from corporate executives and reach buy-side investors after three links in the network. Inside traders earn prodigious returns of 35% over 21 days, with more central traders earning greater returns, as information conveyed through social networks improves price efficiency. More broadly, this paper provides some of the only direct evidence of person-to-person communication among investors.