Decision, Operations & Information Technologies

Permanent URI for this communityhttp://hdl.handle.net/1903/2230

Prior to January 4, 2009, this unit was named Decision & Information Technologies.

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Start date: 2020Subject: search.filters.subject.Business administration

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Now showing 1 - 5 of 5
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    ON THE IMPLICATIONS OF NEW POLICIES, MARQUEE SELLERS, AND GREEN NUDGES IN ONLINE SECONDARY MARKETS FOR DURABLE IT PRODUCTS: EVIDENCE FROM EMPIRICAL STUDIES
    (2021) Alhauli, Abdullah; Gopal, Anandasivam; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The rapid pace of product development in the IT sector has led to a volume surge of product returns, giving rise to critical environmental threats that can potentially have significantly adverse ecological effects. One possible avenue to mitigate these negative effects pertains to the establishment of robust secondary markets for these products, so that their useful life can be enhanced. My dissertation seeks to study multiple aspects aimed at enhancing the efficiency of online secondary markets for durable IT products, using economic and behavioral theories. The first essay examines the extent to which firm policies in the primary market mitigate inefficiencies caused by adverse selection in the secondary market for IT products. I find that policies implemented by firms in the primary market with respect to their products can have beneficial effects in addressing adverse selection in the secondary markets. The second essay studies how adding a marquee seller to a B2B secondary market platform for IT products affects other sellers, in terms of the prices they obtain for comparable products. I show that the entry of a marquee seller has a positive effect on the prices obtained by other sellers on the platform. I further show that this positive effect on final prices is moderated by bidders multi-homing activity, and their level of involvement in the marquee seller’s site. Finally, through behavioral experiments performed on Amazon MTurk, my third essay examines the extent to which the use of behavioral interventions, in the form of green nudges, can enhance the propensity of used IT products being purchased in the secondary market, thereby increasing the lifetime of these products. I find that the efficacy of using green nudges to impact consumer behavior depends on the kind of motivation (i.e., internal versus external motivation) the nudge is delivering. I further find that the effectiveness of green nudges can vary based upon product price and perceived quality, and consumer demographics and latent personalities. Collectively, the findings from these studies in my dissertation provide valuable theoretical as well as practical insights about the effectiveness of different mechanisms for enhancing the efficiency of online secondary markets for durable IT products.
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    EMPIRICAL INVESTIGATION OF USERS’ SUCCESSFUL STRATEGIES IN ONLINE PLATFORMS - EVIDENCE FROM CROWD-SOURCING AND SOCIAL MEDIA PLATFORMS
    (2021) Lysyakov, Mikhail; Viswanathan, Siva; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    With the proliferation and constant growth of online platforms, there has been an increasing interest among academicians and practitioners to understand various aspects of these platforms, including the effective design of platforms, their governance and user engagement. This dissertation seeks to add to this stream of research by leveraging large-scale unstructured data and corresponding data analytics and econometric techniques to examine users’ strategies in online social media and crowdsourcing platforms and gain insights into factors that lead to successful outcomes. The first essay examines the content strategies of closely competing firms on Twitter with a focus on how the similarity/dissimilarity of their content strategies impacts their online outcomes. I find that firms that are more adept at leveraging higher-level social media affordances, such as interactivity, collaboration, and online contests to differentiate their content strategies experience better outcomes as compared to their closest rivals that only leverage the basic technological affordances of social media. The second essay examines successful strategies of users (designers) in a crowdsourcing platform wherein clients post contests to solicit design solutions for a monetary reward. This study uses state-of-the-art deep learning and image analysis techniques to examine the strategies of experienced and less-experienced designers in open contests where later-entrants can potentially leverage information spillovers from earlier design submissions within a contest. I find that while later-entrants typically leverage information spillovers from earlier submissions in a contest, only experienced designers who are able to integrate information from multiple highly-rated early submissions are more likely to be successful. The third essay examines users’ strategies in response to the introduction of an Artificial Intelligence system for logo design in an online crowdsourcing design platform. In analyzing what differentiates successful contestants from the others, I find that the successful contestants significantly increase focus (i.e., the number of re-submissions per contest) and increase the emotional content as well as the complexity of their designs, in response to the introduction of the AI system. Collectively, the findings from these studies add to our understanding of successful strategies in online platforms and provide valuable insights to theory and practice.
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    STUDYING THE IMPACT OF GOVERNMENT PROGRAMS ON HEALTHCARE EFFICIENCY USING ECONOMETRIC MODELS
    (2020) Ren, Ai; Golden, Bruce; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    In this dissertation, we evaluate certain government interventions in several ways. We investigate the impact of the Global Budget Revenue (GBR) program on Length of Stay (LOS) for inpatients in emergency departments (EDs) in Maryland. The GBR program was announced on January 1, 2014 and, as a government mandate, all Maryland hospitals that were not covered by the Total Patient Revenue program were required to participate. Around the same time, many states, including Maryland, adopted Medicaid expansion. To estimate the effect of GBR on LOS in Maryland hospitals, we conduct a difference-in-differences (DID) analysis and consider the impact of Medicaid expansion by using hospitals from West Virginia, Delaware, and Rhode Island, which also adopted Medicaid expansion at the same time, as the control group. We expand the GBR study by adding more controls and using a longer study period. We find that all results support the conclusion that GBR implementation has a negative impact on the time that Maryland inpatients spend in the ED and, the bigger the hospital, the longer the LOS. We conduct a DID analysis and investigate the impact of the GBR program on the wait time for inpatients from admit decision to the time of departure from the ED in Maryland using four control groups based on different assumptions. Our estimates imply that GBR has a negative impact on the wait time of inpatients in the ED. Finally, we provide a comprehensive literature review of articles that used a DID model and were published since 1990 in the top 30 emergency medicine journals listed by the Scimago Institutions Rankings. We show that the top journals in emergency medicine have become more likely to publish DID-related articles. In the sixth chapter, we examine the changes in the number of fatality reports before and after the implementation of the Severe Injury Reporting Program (SIRP) using a DID model. Our study suggests the Occupational Safety and Health Administration (OSHA) should expand its approved state programs so as to increase implementation of the SIRP on the state level rather than the federal level. This could relieve pressure on OSHA’s limited resources while maintaining its commitment to national workplace safety.
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    Design and Operations on the Supply Side of Online Marketplaces
    (2020) Zhang, Wenchang; Elmaghraby, Wedad J; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Online platforms like eBay, Upwork, Airbnb, and Uber have transformed their markets, and many more are about to emerge. The rise of platforms has become one of the predominant economic and social developments of our time. Moreover, it has created many opportunities and challenges for both practitioners and researchers. My dissertation focuses on the design and operations on the supply side of online marketplaces. In particular, I study supply-side levers (e.g., listing policy and information provision policy) in different marketplace context (e.g., auction marketplace and service platform), with the consideration of strategic behavior of market participants and various friction involved in transactions (e.g., participation cost, information asymmetry, and supply adjustment friction). The first essay investigates how a one-sided liquidation auction marketplace maximizes its revenue by managing the supply-side market thickness under an exogenous supply inflow. The second essay examines the operational impacts of service platforms’ information disclosure regarding service providers’ qualities and revealing their mechanisms. The last essay studies whether two-sided marketplaces benefit or suffer from sellers’ quantity competition under unanticipated demand shocks. We further show that marketplaces can maneuver the competition in favorable directions by manipulating the supply adjustment friction. Overall, the findings from the three essays show that marketplaces’ operational levers on the supply side have significant effects on the strategies of all participants, which impacts the marketplaces’ operational performance. The dissertation offers both theoretical insights on the mechanisms of the studied supply-side levers and practical implications on how these levers should be designed and implemented.
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    USING ARTIFICIAL INTELLIGENCE TO IMPROVE HEALTHCARE QUALITY AND EFFICIENCY
    (2020) Wang, Weiguang; Gao, Guodong Gordon; Agarwal, Ritu; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    In recent years, artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), has represented one of the most exciting advances in science. The performance of ML-based AI in many areas, such as computer vision, voice recognition, and natural language processing has improved dramatically, offering unprecedented opportunities for application in a variety of different domains. In the critical domain of healthcare, great potential exists for a broader application of ML to improve quality and efficiency. At the same time, there are substantial challenges in the development and implementation of AI in healthcare. This dissertation aims to study the application of state-of-the-art AI technologies in healthcare, ranging from original method development to model interpretation and real-world implementation. First, a novel DL-based method is developed to efficiently analyze the rich and complex electronic health record data. This DL-based approach shows promise in facilitating the analysis of real-world data and can complement clinical knowledge by revealing deeper insights. Both knowledge discovery and performance of predictive models are demonstrably boosted by this method. Second, a recurrent neural network (named LSTM-DL) is developed and shown to outperform all existing methods in addressing an important real-world question, patient cost prediction. A series of novel analyses is used to derive a deeper understanding of deep learning’s advantages. The LSTM-DL model consistently outperforms other models with nearly the same level of advantages across different subgroups. Interestingly, the advantage of the LSTM-DL is significantly driven by the amount of fluctuation in the sequential data. By opening the “black box,” the parameters learned during the training period are examined, and is it demonstrated that LSTM-DL’s ability to react to high fluctuation is gained during the training rather than inherited from its special architecture. LSTM-DL can also learn to be less sensitive to fluctuations if the fluctuation is not playing an important role. Finally, the implementation of ML models in real practice is studied. Since at its current stage of development, ML-based AI will most likely assistant human workers rather than replace them, it is critical to understand how human workers collaborate with AI. An AI tool was developed in collaboration with a medical coding company, and successfully implemented in the real work environment. The impact of this tool on worker performance is examined. Findings show that use of AI can significantly boost the work productivity of human coders. The heterogeneity of AI’s effects is further investigated, and results show that the human circadian rhythm and coder seniority are both significant factors in conditioning productivity gains. One interesting finding regarding heterogeneity is that the AI has its best effects when a coder is at her/his peak of performance (as opposed to other times), which supports the theory of human-AI complementarity. However, this theory does not necessarily hold true across different coders. While it could be assumed that senior coders would benefit more from the AI, junior coders’ productivity is found to improve more. A further qualitative study uncovers the underlying mechanism driving this interesting effect: senior coders express strong resistance to AI, and their low trust in AI significantly hinders them from realizing the AI’s value.