Smartphone-based contact-tracing apps are a promising solution to help scale up the conventional contact-tracing process. However, low adoption rates have become a major issue that prevents these apps from achieving their full potential. In this paper, we present a national-scale survey experiment (N=1963) in the U.S. to investigate the effects of app design choices and individual differences on COVID-19 contact-tracing app adoption intentions. We found that individual differences such as prosocialness, COVID-19 risk perceptions, general privacy concerns, technology readiness, and demographic factors played a more important role than app design choices such as decentralized design vs. centralized design, location use, app providers, and the presentation of security risks. Certain app designs could exacerbate the different preferences in different sub-populations which may lead to an inequality of acceptance to certain app design choices (e.g., developed by state health authorities vs. a large tech company) among different groups of people (e.g., people living in rural areas vs. people living in urban areas). Our mediation analysis showed that one’s perception of the public health benefits offered by the app and the adoption willingness of other people had a larger effect in explaining the observed effects of app design choices and individual differences than one’s perception of the app’s security and privacy risks. With these findings, we discuss practical implications on the design, marketing, and deployment of COVID-19 contact-tracing apps in the U.S.

The pervasiveness of mobile technologies today has facilitated the creation of massive online crowdsourced and geotagged data from individual users at different locations in a city. Such ubiquitous user-generated data allow us to study the social and behavioral trajectories of individuals across both digital and physical environments. This information, combined with traditional economic and behavioral indicators in the city (e.g., store purchases, restaurant visits, parking), can help us better understand human behavior and interactions with cities. In this study, we take an economic perspective and focus on understanding human economic behavior in the city by examining the performance of local businesses based on the values learned from crowsourced and geotagged data. Specifically, we extract multiple traffic and human mobility features from publicly available data source geomapping and geo-social-tagging techniques and examine the effects of both static and dynamic features on booking volume of local restaurants. Our study is instantiated on a unique dataset of restaurant bookings from OpenTable for 3,187 restaurants in New York City from November 2013 to March 2014. Our results suggest that foot traffic can increase local popularity and business performance, while mobility and traffic from automobiles may hurt local businesses, especially the well-established chains and high-end restaurants. We also find that, on average, one or more street closure (caused by events or construction projects) nearby leads to a 4.7% decrease in the probability of a restaurant being fully booked during the dinner peak. Our study demonstrates the potential to best make use of the large volumes and diverse sources of crowdsourced and geotagged user-generated data to create matrices to predict local economic demand in a manner that is fast, cheap, accurate, and meaningful.

With the pervasiveness of mobile technology and location-based computing, new forms of smart urban transportation, such as Uber & Lyft, have become increasingly popular. These new forms of urban infrastructure can influence individuals' movement frictions and patterns, in turn influencing local consumption patterns and the economic performance of local businesses. To gain insights about future impact of urban transportation changes, in this paper, we utilize a novel dataset and econometric analysis methods to present a quasi-experimental examination of how the emerging growth of peer-to-peer car sharing services may have affected local consumer mobility and consumption patterns.

The pervasiveness of mobile technologies today have facilitated the creation of massive crowdsourced and geotagged data from individual users in real time and at different locations in the city. Such ubiquitous user-generated data allow us to infer various patterns of human behavior, which help us understand the interactions between humans and cities. In this study, we focus on understanding users economic behavior in the city by examining the economic value from crowdsourced and geotaggged data. Specifically, we extract multiple traffic and human mobility features from publicly available data sources using NLP and geo-mapping techniques, and examine the effects of both static and dynamic features on economic outcome of local businesses. Our study is instantiated on a unique dataset of restaurant bookings from OpenTable for 3,187 restaurants in New York City from November 2013 to March 2014. Our results suggest that foot traffic can increase local popularity and business performance, while mobility and traffic from automobiles may hurt local businesses, especially the well-established chains and high-end restaurants. We also find that on average one more street closure nearby leads to a 4.7% decrease in the probability of a restaurant being fully booked during the dinner peak. Our study demonstrates the potential of how to best make use of the large volumes and diverse sources of crowdsourced and geotagged user-generated data to create matrices to predict local economic demand in a manner that is fast, cheap, accurate, and meaningful.

With the growing pervasiveness of the Internet, online search for products and services is constantly increasing. Most product search engines are based on adaptations of theoretical models devised for information retrieval. However, the decision mechanism that underlies the process of buying a product is different than the process of locating relevant documents or objects.