In producing and providing services to local residents, municipal governments increasingly incorporate digital technology into their Information Systems (IS). In these digitally-enhanced government IS, how does digital technology affect social justice in the use and outcomes of the systems? By applying theories of distributive justice and analyzing data collected from Boston's 311 system for residents to request non-emergency services, we have found significant and lasting disparities between wealthy and poor communities in the use of the system's digital channels (mobile app and website).
This study analyzes the effects of community-level socioeconomic deprivations (SED) on public libraries’ book circulation in the Seoul metropolitan area. The study design draws upon the theory of local information landscapes, which explains the relationship between community characteristics and information behavior. Using four-year (2015-2018) open government and public library circulation data, we constructed a socioeconomic deprivation index by adjusting a multi-dimensional deprivation index and generated other variables.
Higher prevalence of triple negative breast cancer (TNBC) in black women with associated poor outcomes due to various disparities is well documented within a single state. We examine multiple states to better understand the state effect on such differences in incidence and prevalence of TNBC in black women.
Many cities in the United States use civic technologies like 311 systems as part of their public service systems for monitoring non-emergency civic issues. These systems have enhanced the city's monitoring capability by diversifying communication channels. However, the data created through these systems is often biased because of differences in people's use of technology (i.e., digital divide) and individuals' behavioral patterns in providing types of information to the systems.
The 311 systems that city officials currently deploy can efficiently detect non-emergency civic issues such as potholes and trash. From a socio-technical perspective, residents can re-appropriate the technology for their own purpose adding new capacities and affordances not initially intended. For example, when Hurricane Irma hit Miami in 2017, residents used 311 systems to report disaster-related issues, which led city officials to adapt the system by creating a new category.
Cultural diversity has been conceptualized and studied in diverse ways. On the one hand, cultural diversity can be conceptualized based on people’s ethnic and national backgrounds. On the other hand, cultural dimensions are defined based on individuals' behaviors and traits. Sociologists further categorize the latter depending on the degree of typicality in cultural artifacts/activities and individuals’ omnivorousness over cultural tastes.
To understand issues about information accessibility within communities, research studies have examined human, social, and technical factors by taking a sociotechnical view. While this view provides a profound understanding of how people seek, use, and access information, this approach tends to overlook the impact of the larger structures of information landscapes that constantly shape peoples access to information.
Today's digital revolution is fostering remarkable innovations. The landscape of innovation is rapidly changing and thus difficult to navigate or study. Understanding broader participation in innovation requires large datasets on the innovation participants and their activities. As organizational and industry boundaries become fuzzy in the digitized world, small data analysis cannot fully explain how boundaries shift and evolve. Open innovation leads to overlapping roles of designer and user, making it inadequate to examine innovation development and adoption separately.
This research will develop a foundational tool for understanding how civic technologies are used and how information inequalities manifest in a city. User data from new civic technologies that reveal inequalities in the information environments of citizens has only recently become available. Since a large portion of data is demographically or geospatially biased due to varying human-data relationships, computational social scientists have used data modeling and algorithmic techniques to adjust the data and remove biases during data-processing.
Natural disasters such as hurricanes, floods or tornadoes affect millions of individuals every year. As a result, governments spend millions of dollars in emergency response allocating resources to mitigate the damages. Effective resource allocation requires a deep understanding of how humans react when a disaster takes place. Due to the multiplicity of human behavior, however, it is not trivial to understand human behaviors at large scale during and after the disaster.