Digital Transformations of Business Operations

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    Estimating the Impact of Asymptomatic Carriers on the spread of Infectious Diseases: An interaction-based Model
    ( 2022-01-04) Ravid, Yaniv ; Seidmann, Abraham
    Epidemiological models are commonly used to predict and describe the spread of a viral outbreak among a population. Many such models use differential equations and transition rates to predict the growth dynamics of the infectious and exposed groups with a greater population. These methods do not distinguish between infectious individuals. In this paper, we propose a new model that includes asymptomatic carriers while holding constant many of the transition rates and assumptions of the classical models. Seeking to replicate realistic outbreak scenarios, we introduce a way to estimate the reproduction number R0 of epidemics and apply these estimations to our model. Our results replicate those described in similar research papers, showing that a small proportion of asymptomatic carriers can be responsible for a majority of the transmissions. We propose possible extensions to our model, underlining the impactful applications it may have on healthcare management and public safety policymaking.
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    Does Telehealth Reduce Rural-Urban Care Access Disparities? Evidence from Covid-19 Telehealth Expansion
    ( 2022-01-04) Sun, Shujing ; Wang, Guihua
    Using hospital claims data, we study the effect of telehealth expansion on the disparities between care access in rural and urban areas during Covid-19. We use urban areas as the control group and compare the changes in patients' access to care before and after the telehealth expansion. We find that the rural-urban disparities in overall access to care (i.e., the total number of visits) remain unchanged after the policy. We further distinguish in-person from telehealth visits and find enlarged disparities in patients' visiting modalities. In particular, urban patients substitute in-person visits with telehealth visits, yet rural patients have a much lower adoption rate of telehealth services and continue with in-person visits. Finally, we perform visit-level analyses and identify patients' social determinants and physicians' characteristics associated with telehealth adoptions.
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    Detecting Red Flag of Workplace Crime Using Mobile Data on Abnormal Usage Activities
    ( 2022-01-04) Wang, Yuting ; Xue, Ling ; Liu, Hefu ; Cai, Zhao
    The purpose of this paper is to investigate how to detect the workplace crime of organizational sales representatives (e.g., sales who work with external customers) through abnormal activities that can be traced by mobile devices and applications. The guardianship capability of organizations is considered as the moderator influencing the monitoring of abnormal usage activities calculated by deep learning. In this study, we conduct event history analysis on the occurrence of workplace crime utilizing a longitudinal panel data set, which comprises 197179 weekly observations in 3 years (2017-2019). Our finding provides evidence that the abnormal activity pattern is an effective signal for identifying workplace crimes. Furthermore, we illustrate how to design monitoring modes based on guardianship capability in order to maximize the effectiveness of mobile monitoring in reducing workplace crimes.
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    Content Spending and Network Quality in Mobile Channels: A Hidden Markov Model of User Engagement and Content Consumption
    ( 2022-01-04) Zhao, Xia ; Huang, Lu ; Wang, Lei ; Yazdani, Elham ; Zhang, Cheng
    Nowadays, individuals increasingly depend on mobile devices and apps for every aspect of their daily lives. Even though the number of mobile users and the time that they spend on mobile apps have grown tremendously, app providers are still struggling with low user engagement and a high attrition rate. This study examines how users’ prior content spending and network delay impact consumers’ mobile content consumption decisions and how the impacts vary in different engagement states. We develop a Hidden Markov Model and calibrate it using a large tapstream data set of individual users’ reading activities from a mobile app provider. We identify three engagement states and heterogeneous impacts of the financial and operational factors. This study will generate important implications on content pricing and user engagement in mobile channels
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    Introduction to the Minitrack on Digital Transformations of Business Operations
    ( 2022-01-04) Seidmann, Abraham ; Zhang, Jie ; Jiang, Yabing