Multi-Dimensional Observational Learning in Social Networks: Theory and Experimental Evidence

Over the last decade, there has been an unprecedented growth of social media platforms (e.g., Instagram, and Pinterest). This growth has resulted in significant increases in the availability of individual-specific information such as holiday pictures, mobile check-ins at restaurants, to everyday purchases. Besides sharing their data, about 72% of Instagram users also made purchase decisions after seeing something on Instagram, with the most common categories being clothing, makeup, shoes, and jewelry . In a similar vein, Pinterest, an image-based social platform, has product rich pins that facilitate users to discover new products: In 2016, 55% of U.S. online users shared that their primary use of Pinterest was to find and shop for products . The prevalence of consumers sharing their purchases on social media platforms (e.g., Instagram, and Pinterest) and the use of this information by potential future consumers have substantial implications for online retailing. Consumers can observe the purchase information shared by their “friends” or “strangers.” The product offerings on a social network are diverse and led us to integrate products with different attributes. One approach to classify product types by distinct characteristics is to consider how these attributes drive consumer choice, i.e., classification of products with vertically and horizontally differentiated attributes. In this study, we examine how product characteristics and the type of information provider jointly moderate the purchase decision in a social network setting. We first propose an analytical observational learning framework integrating the impact of product differentiation and social ties. Then, we use two experimental studies to validate our analytical results and provide additional insights. Our key findings are that the effect of learning from strangers is stronger for vertically differentiated products than for the horizontally differentiated product. However, the impact of learning from friends does not depend on whether the underlying product is horizontally or vertically differentiated. What is more interesting is the nuanced role of social ties: For horizontally differentiated products, the effect of learning increases with the strength of social ties. In addition, “contact-based” tie strength is more important than “structure-based” tie strength in accelerating observational learning. These findings can motivate online retailers to generate alternative strategies for increasing product sales through social networks. For example, online retailers offering horizontally differentiated products have strong incentives to cooperate with social media platforms (e.g., Instagram and Pinterest) in encouraging customers to share their purchase information

A Twitter-Based Prediction Market: Social Network Approach

Information aggregation mechanisms are designed explicitly for collecting and aggregating dispersed information. Prediction markets represent one of the best examples of how this kind of wisdom of the crowds can be used. We use a Twitter-based prediction market to suggest that carefully designed market mechanisms can bring to light trends in dispersed information that improves the accuracy of our predictions. The information system we are developing combines the power of prediction markets with the popularity of Twitter. Simulation results show that our network-embedded prediction market can produce better predictions using information exchange in social networks and can outperform other prediction markets that do not use social networks. We also demonstrate that as cost decreases and more and more agents acquire information, the prediction market prices fully incorporate all available information, and the forecasting performance of the network-embedded prediction market is better

Information Exchange in Prediction Markets: How Social Networks Promote Forecast Efficiency

This paper studies the effects of social networks on the performance of prediction markets with endogenous information acquisition. We provide a game-theoretic framework to resolve the question: Can social networks and information exchange promote the forecast efficiency in prediction markets? Our study shows that the use of social networks could be detrimental to forecast performance when the cost of information acquisition is high. Although social networks can provide internal communications among participants, they reduce the incentive to acquire information because of free riding. We also study the effects of social networks on information acquisition in prediction markets. In the symmetric Bayes-Nash Equilibrium, all participants use a threshold strategy, and the equilibrium action of information acquisition is decreasing in the number of participant\u27s friends and increasing in the network density

Beauty Contest and Social Value of Fintech: An Economic Analysis

The past decade has witnessed a financial technology (Fintech) revolution. With the advent of Fintech in trading markets, many technology startups are using social media to gauge investors’ sentiment, as well as to detect events quickly, which in turn could impact stock prices and affect the efficiency of financial markets. Another example of Fintech that is enabled by the aggregation of opinions is social trading platforms. These platforms are financial counterparts of social networks where people can create an online profile and share information about investments and trading in financial instruments such as stocks and cryptocurrencies with other members of the platform. Online platforms like Sharewise and Estimize specialize in aggregating the opinions and predictions of all its users to come up with a target price for a stock, thus enabling the users to gain market insights from public opinion. This shift in trading has led to the emergence of a new class of investors who trade not only based on their own knowledge (or beliefs) about the market but also on that of the crowd’s opinion. Investors who follow public information about the fundamental price and their own private information are classified as first-order-beliefs traders while investors who use private information along with information gained from using such Fintech product are classified as higher-order-beliefs traders. The Fintech product captures the sentiment of other investors and experts by aggregating their opinions and predictions expressed on various social media platforms. Following the insight of Keynes (1936) on financial markets being akin to a beauty contest, where some people grade contestants based on who they think will be attractive to others, we model the market where a certain fraction of traders is employing the beauty-contest paradigm, using the services offered by Fintech firms, to form higher-order beliefs. We then establish the equilibrium in the market and examine its different properties. We also analyze how higher-order beliefs affect market efficiency and social welfare of investors. In the process, we answer the following research questions: is this shift beneficial for the market (in terms of efficiency) and the individual investors? And, if so, under what conditions and circumstances? Further, is there any limit to the number of Fintech investors (as a fraction of all investors) in the market so that social welfare is maximized? We find that higher-order beliefs tend to reduce market efficiency because public information is over-weighted. Increased precision of private information always enhances market efficiency; however, when public information is relatively noisy, increased precision of public information is detrimental to market efficiency. We also examine the effect of relative precision of public information to private information and fraction of Fintech investors in the market on ex-ante wealth of investors. Since accounting disclosure is a main source of public information, our results highlight that the use of Fintech in financial trading can dramatically affect the optimal level of accounting disclosure (i.e., transparency)

Get a Word in Edgewise: Post Character Limit and Social Media-Based Customer Service

In this paper, we study the role of extending character limits on firm responses on social media. By leveraging a natural experiment setting: the unexpected increase in post character limit on Twitter, we empirically investigate the impact on the linguistic styles of social media-based customer service responses. Using a Regression Discontinuity in Time Design and leveraging a panel dataset, our results suggest that extending character limits influences firm to change the linguistic styles in their responses which could influence consumers' perceptions. Our results show that extending post-character limits significantly reduces the readability ease of firm responses, on average, while increasing the concreteness and personal closeness scores of these responses, on average. We show that these changes were effective in influencing customer satisfaction

Manipulation: Online Platforms’ Inescapable Fate

Online platforms are prone to abuse and manipulation from strategic parties. For example, social media and review websites suffer from the presence of opinion spam and fake reviews. Applying the economic concept of rational expectation equilibrium (REE), we explore the impact of manipulation on consumer welfare in a Twitter-like environment. We argue that the REE outcome can be decomposed into a firm-centric effect and a rational expectation effect, and the relative strength of these effects determines the final level of manipulation. We also examine the effect of competition on firms’ manipulation levels. We find that the combination of a competition effect and a rational expectation effect determines the overall effect of competition on strategic manipulation. This research sheds light on the reliability of opinion mining, and contributes to our understanding of strategic manipulation in the context of sentiment analysis

Stimulating Feedback Contributions Using Digital Nudges: A Field Experiment in a Real-time Mobile Feedback Platform

In the contemporary remote work environment, the demand for effective and timely feedback has significantly grown. Despite the adoption of feedback systems, many employees still find these platforms lacking in delivering meaningful insights. This study delves into the potential of digital nudges—reminder notifications sent to users—as a strategy to enhance feedback contributions on mobile platforms. A randomized field experiment was conducted in collaboration with a prominent organization, exploring variations in nudge send times and the emphasis on task significance. Spanning five weeks, the experiment evaluated the efficacy of these nudges in fostering feedback engagement among employees. Our findings indicate that the timing, content of nudges (i.e., task significance message), and a combination of these two, can significantly influence feedback behavior. The study\u27s findings have potential implications for organizations aiming to bolster their feedback systems, making them more responsive and effective in the digital age

Online Content Consumption: Social Endorsements, Observational Learning and Word-of-Mouth

The consumption of online content can occur through observational learning (OL) whereby consumers follow previous consumers’ choices or social endorsement (SE) wherein consumers receive content sharing from their social ties. As users consume content, they also generate post-consumption word-of-mouth (WOM) signals. OL, SE and WOM together shape the diffusion of the content. This study examines the drivers of SE and the effect of SE on content consumption and post-consumption WOM. In particular, we compare SE with OL. Using a random sample of 8,945 new videos posted on YouTube, we collected a multi-platform dataset consisting of data on video consumption and WOM from YouTube and data on tweet sharing of the video from Twitter. Applying a panel vector autoregression (PVAR) model, we find that OL increases consumption significantly more than SE in the short run. However, SE has a stronger effect on content consumption in the long run. This can be attributed to the impact of SE on WOM signals, which also increase content consumption. While OL and SE leads to similar amount of positive WOM, SE generates significantly more negative WOM than OL. Our results also show that SE is driven by WOM (i.e., likes and dislikes) but not content popularity. We further confirm the effects of OL vs. SE on content consumption and WOM using a randomized experiment at the individual consumer level. Implications for content providers and social media platforms are derived accordingly

Do Masks Protect Children? Evidence from Florida’s Mask Mandate Ban Using Large-Scale School Transmission Data

Our study examines the causal impact of mask mandates on COVID-19 transmission in elementary and middle schools using a natural experiment in Florida. While randomized controlled trials (RCTs) have been the gold standard for causal investigation, they face challenges such as lower compliance rates and typically focus only on the direct impact on mask wearers, overlooking the potential benefits of transmission reduction. Our natural experiment overcomes these issues, providing a broader view of mask mandates’ effects. The results show a 20.6% increase in COVID-19 cases when mask mandates are banned. We also explore the moderating effects of school size, search volume for “mask,” and racial and poverty groups on the impact of the mask ban. Our study underscores the critical role of mask mandates and showcases the potential of utilizing publicly accessible data to generate insights on significant societal issues – a principle at the core of crowd-based platforms