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Ace Vo, Ph.D. - Loyola Marymount University. Los Angeles, CA, US

Ace Vo, Ph.D.

Associate Professor of Information Systems and Business Analytics, College of Business Administration | Loyola Marymount University



You can contact Ace Vo at ace.vo@lmu.edu.

The expertise of Ace Vo includes data mining, spatial analytics, and big data machine learning, especially in the healthcare informatics domain. With a transdisciplinary approach to his work, he continually seeks ways to integrate and augment different domains’ strengths in order to bridge the gap between practice and theory and solve the most pressing problems. Professor Vo joined LMU in fall 2019. He previously taught at San Francisco State University. His research has been featured in several premier journals in information systems such as Decision Support Systems, Information Systems Frontiers, and Journal of Computer Information Systems. He has consulted and worked in various fields, including online advertising, manufacturing, hospitality, and healthcare. In addition, he holds several well-regarded professional certifications, one of which is the Project Management Professional (PMP®) and the Amazon Web Services (AWS) Solution Architect.

Education (3)

Claremont Graduate University: Ph.D., Information Systems and Technology 2017

California State University, Fullerton: M.S., Information Systems 2012

University of Arizona: B.S., Information Systems and Business Management 2008


Areas of Expertise (3)

Big Data Machine Learning

Data Mining

Spatial Analytics

Articles (10)

Title: The Association Between Social Determinants of Health and Population Health Outcomes: Ecological Analysis

JMIR Public Health and Surveillance Journal


This study aimed to investigate the ecological association between SDOH factors and population health outcomes at the census tract level and the city level. The findings of this study can be applied to support local policy makers in efforts to improve population health, enhance the quality of care, and reduce health inequity.

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A Taxonomy for Risk Assessment of Cyberattacks on Critical Infrastructure (TRACI)

Communications of the AIS


Cybercrime against critical infrastructure such as nuclear reactors, power plants, and dams has been increasing in frequency and severity. Recent literature regarding these types of attacks has been extensive but due to the sensitive nature of this field, there is very little empirical data. We address these issues by integrating Routine Activity Theory and Rational Choice Theory, and we create a classification tool called TRACI (Taxonomy for Risk Assessment of Cyberattacks on Critical Infrastructure). We take a Design Science Research approach to develop, evaluate, and refine the proposed artifact. We use mix methods to demonstrate that our taxonomy can successfully capture the characteristics of various cyberattacks against critical infrastructure. TRACI consists of three dimensions, and each dimension contains its own subdimensions. The first dimension comprises of hacker motivation, which can be financial, socio-cultural, thrill-seeking, and/or economic. The second dimension represents the assets such as cyber, physical, and/or cyber-physical components. The third dimension is related to threats, vulnerabilities, and controls that are fundamental to establishing and maintaining an information security posture and overall cyber resilience. Our work is among the first to utilize criminological theories and Design Science to create an empirically validated artifact for improving critical infrastructure risk management.

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Showcase: A Data-Driven Dashboard for Federal Criminal Sentencing

Journal of the AIS


The main purpose of the Sentencing Reform Act of 1984 was to provide more uniformity in sentencing and reduce inter-judge disparity. Subsequently, the Act created the federal sentencing guidelines to offer judges a possible sentencing range for offenses. However, since these recommendations were based on historical data, the guidelines amplified existing biases and increased inequality and disproportionate sentencing of minorities. To address this problem, we developed an artifact called “ShowCase”—a data-driven dashboard—that is grounded in penal theory, organizational context theory, social bonds theory, and triangulation notion in design theory. The artifact helps judges make fairer and more objective decisions by integrating a variety of data points. We used a design science research methodology and mixed methods to guide the development and evaluation of the proposed dashboard. Our research inquiry revealed what legal and extralegal factors contribute to more equitable judicial decisions. We also found support for integrating data science and more diverse viewpoints in the sentencing process. Our study shows that a validated data-driven dashboard can be used to promote fairness, objectivity, and transparency in the criminal justice system.

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Examining the Maturity of Bitcoin Price through a Catastrophic Event: The Case of Structural Break Analysis During the COVID-19 Pandemic

Finance Research Letters


In this research, we examine the maturity of Bitcoin by using structural break analysis to examine Bitcoin price. Our results reveal that the number of structural breaks of Bitcoin has remained the same at five. The Solidification phase started 8 months prior to the onset of the pandemic, signifying the small effect of the pandemic on Bitcoin price. During the pandemic, Bitcoin was more susceptible to underlying economic factors than to either COVID-related variables alone or to a combination of both factors. We conclude that Bitcoin has reached a certain level of maturity as an investment vehicle.

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Development of a mobile training app to assist radiographers’ diagnostic assessments

Health Informatics Journal


The current study reduced the time lag between performing a diagnostic assessment and identifying a critical finding in CT and MRI exams through improving radiographers’ abilities to identify those critical findings. Radiographers’ diagnostic assessments in CT and MRI exams were used to develop a mobile training application with the aim to improve radiographers’ awareness of critical findings. The current research used data analytics to examine radiographers’ interpretation of imaging studies from a privately owned medical group in Israel. During the project, the radiographers’ ability to identify critical findings improved. Implementation of the mobile training program yielded positive results where the knowledge gap was reduced and time to identify critical cases was decreased. Specifically, this study showed that radiographers can be trained in ways that enhance their involvement with radiologists to provide high quality services and improve treatment. Ultimately, this gives patients higher quality of care and safer treatment.

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Examining Bitcoin and Economic Determinants: An Evolutionary Perspective

Journal of Computer Information Systems


After Nakamoto introduced Bitcoin in 2008 as an alternative online payment system, it became an appealing investment vehicle and an attractive area of study for many researchers. Although much research has been done on the valuation of Bitcoin, comparatively little treatment has been spent on Bitcoin’s relationship to established economic indicators, at least in IS research. This study examines Bitcoin across its history using a time-series structural break analysis to differentiate five distinct periods within Bitcoin’s evolution. Data obtained includes the open-high-low-close and volume Bitcoin trading data by the minute from October 15, 2010 until January 1, 2020. Within each period, we examine Bitcoin’s closing price in relation to an established set of economic indicators that encompass economic health, long-term economic stability, monetary policy, and investor sentiment. We find that Bitcoin has matured from a speculative trading mechanism to an independent investment instrument that is responsive to underlying macroeconomic factors.

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Identifying socially vulnerable regions with persistent low accessibility to emergency care through a spatial decision framework

Journal of Decision Systems


During an emergency, areas with high social vulnerability suffer the most. Identifying vulnerable areas with low access to emergency services will aid in prevention and response efforts when hazardous events strike. This paper utilises both the Two-Step Floating Catchment Area and the Enhanced Two-Step Floating Catchment Area methodologies of Geographic Information Systems to measure the accessibility levels for three emergency response thresholds: zero to four minutes, four to eight minutes, and eight to fifteen minutes. An accessibility measurement using the combination of those three response times, from zero to fifteen minutes, is also computed and studied. After investigating the accessibility measurements in light of the Centre for Disease Control’s Social Vulnerability Index, we identified seven tracts in Los Angeles County that are persistently very high in social vulnerability and very low in accessibility. The findings in this research will help to optimise resource utilisation and planning efforts during an emergency.

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Developing a High Frequency Algorithmic Trading Strategy for Cryptocurrency

Journal of Computer Information Systems


Cryptocurrency such as Bitcoin is a rapidly developing phenomenon in financial technology with considerable research interest but is understudied. In this research article, we use a Design Science Research paradigm to create a high-frequency trading strategy at the minute level for Bitcoin using six exchanges as our Information Technology artifact. We created financial indicators and utilized a machine learning (ML) algorithm to create our strategy. We provided two sets of evaluation. First, we evaluated this strategy against another popular ML algorithm and found our algorithm performed better on the average. Second, we analyzed the economic benefits using the strategy against out-of-sample trading in foreign exchange currency. We presented both descriptive and prescriptive contributions to Design Science Research via the development and testing of our artifacts.

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Designing Utilization-based Spatial Healthcare Accessibility Decision Support Systems: A Case of a Regional Health Plan Access

Decision Support Systems


In the U.S., myriad healthcare reforms have begun to show some positive effects on enabling “potential access”. One facet of healthcare access, “having access”, which is the availability and accessibility of health services for the surrounding populations, has not been adequately addressed. Research regarding “having access” is presently championed by a family of methods called Floating Catchment Area (FCA). However, existing scholarship is limited in integrating non-spatial factors within the FCA methods. In this research, we propose a novel utilization-based framework as the first attempt to adopt the Behavioral Model of Health Services Use as a theoretical lens to integrate non-spatial factors in spatial healthcare accessibility research. The framework employs a unique approach to derive categorical and factor weights for different population subgroup's healthcare needs using predictive analytics. The proposed framework is evaluated using a case study of a regional health plan. A Spatial Decision Support System (SDSS) instantiates the framework and enables decision makers to explore physician shortage areas. The SDSS validates the practicality of the proposed utilization-based framework and subsequently allows other FCA methods to be implemented in real-world applications.

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A conceptual framework for quality healthcare accessibility: a scalable approach for big data technologies

Information Systems Frontiers


Healthcare accessibility research has been of growing interest for scholars and practitioners. This manuscript classifies prior studies on the Floating Catchment Area methodologies, a prevalent class of methodologies that measure healthcare accessibility, and presents a framework that conceptualizes accessibility computation. We build the Floating Catchment Method General Framework as an IT artifact, following best practices in Design Science Research. We evaluate the utility of our framework by creating an instantiation, as an algorithm, and test it with large healthcare data sets from California. We showcase the practical application of the artifact and address the pressing issue of access to quality healthcare. This example also serves as a prototype for Big Data Analytics, as it presents opportunities to scale the analysis vertically and horizontally. In order for researchers to perform high impact studies and make the world a better place, an overarching framework utilizing Big Data Analytics should be seriously considered.

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