BIG DATA OPPORTUNITIES AND CHALLENGES: THE CASE OF BANKING INDUSTRY

The banking industry, with a large customer base and their use of mobile and other emerging technologies, has seen a surge in transactions leading to rapid generation of huge amount of data. This large amount of data presents great opportunities to the banking industry. At the same time, the industry faces huge challenges in managing the plethora technologies that are available to execute Big Data projects. Based on initial investigation, there is a gap in literature that clearly examines how the banking industry is leveraging the potentials of Big Data and challenges being encountered. Using a case study with sample of three selected banks, this study seeks to fill the gap by investigating, at a more granular level, how the Banking industry is using and managing Big Data. Findings will contribute to knowledge and practice by increasing our understanding of Big Data implementation and management techniques from Banking Industry’s perspective

Quality of Life Explorer Prototype to Address Socio-economic Problems: A Design Science Approach

This study develops a visualization Proof of Concept that is aimed at improving the quality of life for the city in the South-Eastern part of US as a case study. Using design science research method, we create interactive visual maps called the quality of life explorer (QoLE) for improving the livelihood of residents of the city under study. We then used analytics techniques to customize the map and enhance its visualization and interactivity capabilities in a way that is simple to understand and use. Furthermore, the QoLE website has a built-in capability that allows report generation as well as increase interactivity of Map and data distribution tables. The outcome of this study will help the city authorities and residents to better visualize the data in a smarter and realistic way so that they can make informed decisions in redeveloping their city. More specifically, the study outcomes will help the City authorities and stakeholders integrate the disparate data into a centralized location for easy accessibility and use for decision making

An investigation of analytics and business intelligence applications in improving healthcare organization performance: a mixed methods research

The healthcare ecosystem in the US is currently undergoing series of refinement and reformation due to the need to (i) improve quality of care and (ii) reduce cost. To achieve their key objective, healthcare organizations (HCOs) currently face a fundamental challenge: how to best use or optimize limited resources while providing better care and services to patients? The answer to this question might lie within HCO’s massive data and the ability to identify and apply appropriate analytics and business intelligence (A&BI) techniques and technologies to discern and extract relevant information and knowledge from that data. However, despite the increasing interest in the implementation and utilization of A&BI techniques and technologies by various organizations to improve operational efficiencies and financial performance, HCOs still lag behind other sectors in the adoption and use of A&BI capabilities. Motivated by the “data rich but information poor” syndrome currently facing HCOs, this dissertation applies a mixed method research–case study (interpretivist) and survey (positivist) – to investigate how healthcare organizations can leverage A&BI techniques and technologies to improve their overall performance. In achieving this objective, I illustrate an exemplar of how A&BI techniques and technologies can effectively be applied by specifically answering this high-level research question (RQ): How can A&BI techniques, methods, and technologies be developed and leveraged to improve performance in healthcare organizations? This high-level RQ has been broken down into four sub-questions that will be answered in two different studies in this dissertation. In the first study, I investigate what combination of A&BI techniques and technologies HCOs are currently applying to create value. This study was conducted by using content/literature analysis and case study methods in a large healthcare organization. The second study builds on the first study to investigate, using both interview and survey data, how A&BI capabilities can be developed, cultivated and nurtured as a core competency or capability that significantly helps improve healthcare organizations’ overall performance (such as cost reduction, quick access to providers and treatment, effective diagnostics, etc.). I found very novel and interesting results in both studies that not only address the research questions, but also provide significant theoretical and practical contributions. Major contributions of study 1 include: revising and remodeling of an outdated healthcare value chain (HCVC) framework that is more realistic and applicable to current care delivery practices in the healthcare industry and mapping of A&BI capabilities to the different domains of the revised HCVC framework. Study 2 provides theoretical contribution to the existing literature by conceptualizing and empirically validating A&BI capability as a third-order multi-dimension construct and its significant influence on performance

Giving Voice to the Voiceless: The Use of Digital Technologies by Marginalized Groups

This paper reports on a workshop hosted at the University of Massachusetts Amherst in September, 2018. The workshop, called “Giving Voice to the Voiceless: The Use of Digital Technologies by Marginalized Groups”, focused on discussing how marginalized groups use digital technologies to raise their voices. At the workshop, a diverse group of scholars and doctoral students presented research projects and perspectives on the role that digital technologies have in activist projects that represent marginalized groups that have gained momentum in the last few years. The studies and viewpoints presented shed light on four areas in which IS research can expand our understanding about how marginalized groups use digital technologies to address societal challenges: 1) the rise of cyberactivism, 2) resource mobilization for cyberactivism, 3) cyberactivism by and with marginalized groups, and 4) research methods for examining how marginalized groups use digital technologies

Whole genome sequencing and spatial analysis identifies recent tuberculosis transmission hotspots in Ghana

Whole genome sequencing (WGS) is progressively being used to investigate the transmission dynamics of; Mycobacterium tuberculosis; complex (MTBC). We used WGS analysis to resolve traditional genotype clusters and explored the spatial distribution of confirmed recent transmission clusters. Bacterial genomes from a total of 452 MTBC isolates belonging to large traditional clusters from a population-based study spanning July 2012 and December 2015 were obtained through short read next-generation sequencing using the illumina HiSeq2500 platform. We performed clustering and spatial analysis using specified R packages and ArcGIS. Of the 452 traditional genotype clustered genomes, 314 (69.5%) were confirmed clusters with a median cluster size of 7.5 genomes and an interquartile range of 4-12. Recent tuberculosis (TB) transmission was estimated as 24.7%. We confirmed the wide spread of a Cameroon sub-lineage clone with a cluster size of 78 genomes predominantly from the Ablekuma sub-district of Accra metropolis. More importantly, we identified a recent transmission cluster associated with isoniazid resistance belonging to the Ghana sub-lineage of lineage 4. WGS was useful in detecting unsuspected outbreaks; hence, we recommend its use not only as a research tool but as a surveillance tool to aid in providing the necessary guided steps to track, monitor, and control TB

Genomic epidemiological analysis identifies high relapse among individuals with recurring tuberculosis and provides evidence of household recent TB transmission in Ghana

OBJECTIVE: We investigated the cause of recurring tuberculosis (rcTB) among pulmonary TB participants recruited from a prospective population-based study conducted between July 2012 and December 2015. METHODS: Mycobacterium tuberculosis complex isolates obtained from rcTB cases were characterized by standard mycobacterial genotyping tools in addition to whole genome sequencing, followed by phylogenetic analysis to assess strain relatedness. RESULTS: A greater proportion (58.3%, 21/36) of individuals with rcTB episodes had TB recurrence within 12 months post treatment. Only 19.4% (7/36) of participants with rcTB harbored a strain with isoniazid (INH) resistance at baseline of which 29% (2/7) were additionally resistant to rifampicin. However, 27.8% (10/36) harbored an INH resistant strain upon recurring of which 40% (4/10) were MDR-TB strains. Recurrent TB was attributed to relapse (same strain) in 75.0% (27/36) of participants with 25.0% (9/36) attributed to re-infection. CONCLUSION: Our findings indicate that unresolved previous infection due to inadequate treatment may be the major cause of rcTB

Formulation and analysis of pedestrian safety problems using Bayesian network model

Lee, EarlCauses of pedestrian road accident have been a major concern to transportation engineers and other road safety professionals despite all efforts being applied to alleviate this problem. Although studies have aimed at modeling and analyzing the causes of pedestrian road accidents, the bulk of these studies have been found to be too stochastically oriented and more macroscopic than it is necessary. Consequently, the existing models seldom incorporate the interactions between pedestrians and their immediate environment. In this study, pedestrian crossing behavior during spring and summer season has been thoroughly investigated using Bayesian network modeling technique. The model was constructed with variables known to influence pedestrian crossing behavior either directly or indirectly. Stages of the model building process including Graphical Level (GL), Information or Qualitative Level (IL) and Quantitative Level (QL) have been discussed and implemented to extract useful information from both observed data and data elicited from stakeholders‘ opinion as well as experts‘ experience. The robustness of the Bayesian network model is compared based on its ability to produce physically meaningful results that truly reflects realistic behavior of a system. The model‘s results show that pedestrians often exhibit rational crossing behavior than they do irrationally and such an attitude is found to be influenced mostly by their own motives and less by external factors even though roadway environment did not favored them. Also, a sensitivity analysis carried out revealed that signal timing phase length is the most influential parameter that affects pedestrian crossing behavior.University of Delaware, Department of Civil and Environmental EngineeringM.C.E

Biases in Online Opinion Platforms: A Literature Review and Future Direction

In online opinion platforms, heuristic processing, as opposed to systematic cognitive processing, is a common way people adopt to cope with information overload. Under this circumstance, users make fast and easy judgments, which however may involve more errors or bias (Reisberg 2018). As such, this research-in-progress is being conducted to address two main goals. First, we examine the definition, antecedents, and consequences of online cognitive bias, thereby articulating what existent cognitive biases are. Second, we consider what we don’t’ know about online cognitive bias and translate these gaps into opportunities for future study. To accomplish these objectives, we conduct an extensive literature review in the Basket of Eight journals, by reviewing publications for 10 years. Based on our current work, we suggest that more research can be done in a broader range of online opinion sharing platforms covering more online cognitive bias types