Digital Transformations of Business Operations

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    Platform Coordination of the Sponsored Contents: A Game-Theoretical Analysis
    ( 2023-01-03) Zhang, Xu ; Dou, Yifan
    The prosperity of the content platforms (e.g., YouTube and TikTok) in the recent years has provided a novel channel of media exposure for advertisers. This paper develops a game-theoretical model to examine the role of platform advertising through both the platform itself and the content creators, i.e., the sponsored content. Interestingly, we find that the platform owner should allow both advertising channels to coexist, even they will affect the viewership negatively. We also extend our model in multiple ways, and discover that platform owner should prohibit sponsored content if the content quality would compromised with the sponsored content. We also study the roles of the creator contract and platform competition.
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    Does Health Information Exchange Improve Long-Term Care Service Quality? Evidence from the Panel Data Analysis of the U.S. Long-term Care Facilities
    ( 2023-01-03) Wan, Fang ; Xu, Huiwen ; Seidmann, Abraham
    This paper examines the impact of health information exchange (HIE) on the service quality of long-term care (LTC) facilities based on a five-year period (2013-2017) panel data of the U.S. LTC facilities. Our results show a reverse impact of the HIE adoption on the readmission rate of LTC facilities. The readmission rate of an LTC facility with an operational HIE is reduced by 2% on average as compared to the rate of a facility without operational HIE. We also estimate the heterogeneous effect of HIE by two innovative healthcare ITs (EHR and Telemedicine). We find that the applications of EHR and Telemedicine in LTC facilities are still at a very early stage. Our findings empirically demonstrate the importance of promoting effective data exchange in LTC facilities as well as improving the use of EHR and Telemedicine to increase the value that HIE can create.
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    Redesigning Chronic Care Delivery Using Mobile Health Technology
    ( 2023-01-03) Agnihothri, Saligrama ; Rajan, Balaraman ; Cui, Leon
    Typical management of chronic conditions is through sporadic office visits. But health indicators (such as blood pressure) can fluctuate significantly within a day. The infrequent office visits, however, offer the provider little information about the medical history of the patient between office visits resulting in delayed and sometimes inappropriate interventions. Providing the right product (making appropriate interventions) at the right place (patient's location) at the right time (before the worsening condition leads to a costlier intervention) is the objective of effective supply chain management. Use of mobile health (mHealth) technology in clinical care can help achieve all three objectives. mHealth enables continuous monitoring of measurements resulting in bidirectional information flow between providers and patients, thereby reducing information asymmetry. Our study examines redesigning of chronic care delivery using mHealth. It is important to make sure the redesigned delivery process is both efficient (reduces cost) and effective (improves patient health). In this paper we first present a big picture of the redesigned care delivery process. We then show how this delivery process can improve patient health by analyzing a panel dataset of 1627 patients. We examine the relationship between use of mobile health applications (to remotely upload measurements and receive physician intervention) and quality of care delivery (as measured by blood pressure readings) for hypertensive patients. We observe the blood pressure readings to decrease with frequency of app usage and time since adoption. With the use of mHealth apps increasing in the post COVID-19 era, our analysis indicates an efficient use of physician's time and an increased role for support-staff under the supervision of the physician. The chronic care delivery process can therefore be redesigned with the help of mHealth, improving patient health and reducing cost for both patients and providers.
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    Cryptocurrency Rewards and Crowdsourcing Task Success
    ( 2023-01-03) Meng, Shan ; Zhao, Xia ; Zhao, Xi
    Crowdsourcing task success depends on the contributions of developers. How to identify capable developers and motivate them to actively contribute to a task is a challenging issue. This study investigates how the use of cryptocurrency rewards, i.e., the choices of stablecoins and unstablecoins affects the crowdsourcing task success, and how the relationship depends on task difficulty. Based on 3858 crowdsourcing tasks, we find that the use of unstablecoins reduces the number of participating contributors and extends the time period of having the first contributor, but has no significant effect on the likelihood of task success. In addition, task difficulty alleviates the negative effect of the unstablecoins on the number of participants. Our study potentially provides important implications for the use of cryptocurrency tokens as task rewards.
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    When Machines Will Take Over? Algorithms for Human-Machine Collaborative Decision Making in Healthcare
    ( 2023-01-03) Ahsen, Eren ; Ayvaci, Mehmet ; Mookerjee, Radha
    Artificial intelligence (AI) has increasingly become a popular alternative for performing tasks that are typically performed by humans. Mammography imaging is one context in which the role of AI is growing. Some experts claim that, with recent advancements in image processing algorithms and the increasing availability of data, AI will replace radiologists. Others argue that the rise of AI will change how diagnostic tasks are allocated, eventually paving the way for human-machine collaborative decision-making. In this research, we solve a hospital’s AI acquisition problem for mammography imaging and redesign its operations for human-computer collaborative decision-making. To that end, we propose an optimization model for the hospital that minimizes costs related to mammography screening and determines whether and when a complete automation (AI alone) strategy or a delegation (collaboration between humans and machines) strategy is preferable to an expert-alone strategy. We find that the disease incidence relative to the ratio of follow-up against liability costs is an important determinant of whether the delegation strategy is preferable to the automation strategy. In addition, reductions in algorithmic cost could either result in the delegation (sharing of work between humans and machines) or full automation depending on the performance of the algorithm. Our work has implications beyond radiology imaging for the design of work in the AI era and in the human-machine collaboration context.
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    Get a Word in Edgewise: Post Character Limit and Social Media-Based Customer Service
    ( 2023-01-03) Al Balawi, Ramah ; Hu, Yuheng ; Qiu, Liangfei
    In this paper, we study the role of extending character limits on firm responses on social media. By leveraging a natural experiment setting: the unexpected increase in post character limit on Twitter, we empirically investigate the impact on the linguistic styles of social media-based customer service responses. Using a Regression Discontinuity in Time Design and leveraging a panel dataset, our results suggest that extending character limits influences firm to change the linguistic styles in their responses which could influence consumers' perceptions. Our results show that extending post-character limits significantly reduces the readability ease of firm responses, on average, while increasing the concreteness and personal closeness scores of these responses, on average. We show that these changes were effective in influencing customer satisfaction.
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    Introduction to the Minitrack on Digital Transformations of Business Operations
    ( 2023-01-03) Zhang, Jie ; Jiang, Yabing ; Seidmann, Abraham
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    Pick the Right Tactics When Online Sales Go Live: An Empirical Analysis of Livestreaming for Amazon Sellers
    ( 2023-01-03) Xu, Lingyi ; Seidmann, Abraham ; Soltanieh-Ha, Mohammad
    Using livestreaming to boost sales has become an essential strategy to achieve deeper interactions with customers for many large e-commerce platforms worldwide. Existing livestreaming literature has looked at multiple Chinese e-commerce platforms but not enough attention has been paid to the U.S. market. This study investigates consumer behaviors and the promotion efficacy in the Livestream setting on Amazon Live. We analyze the time patterns of customer engagement and explain why sellers should use different promotion strategies for weekdays and for weekend streamers. Besides, we present evidence that the average video display time per product is crucial for the livestream promotion efficacy and suggest optimal time-exposure intervals as a benchmark for sellers to align with.
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    Does Pre-login Search Matter? Evidence from a Mobile Commerce Platform
    ( 2023-01-03) He, Qiaodan ; Zhang, Luna ; Zhang, Dawei ; Yao, Yuliang
    An increasing number of consumers enjoy shopping through mobile devices. When consumers use a mobile app, they can choose whether to log in with their accounts. We argue that pre-login search plays a critical role in affecting consumers’ purchase decisions, although it has largely been overlooked in the literature. Using clickstream data, we adopt different econometric models to examine whether and how pre-login search affects the likelihood of purchase. Our results show that pre-login search behaviors are as important as post-login search to consumers’ purchase decisions. We also demonstrate that consumers’ purchase propensity increases at a diminishing rate with an increasing search effort during both pre- and post-login periods. Based on recommender systems (RSs) and paradox of choice theory, our results contribute to the burgeoning literature on consumer behavior in mobile commerce and provide novel insights to the strategic usage of RSs. Finally, we discuss theoretical and managerial implications.
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    Does Telemedicine Affect Physician Decisions? Evidence from Antibiotic Prescriptions
    ( 2023-01-03) Kim, Ti ; Sun, Shujing ; Wang, Guihua
    Telemedicine has long been of interest to the U.S. general public. Yet, despite the advent of high-speed internet and mobile device technology, telemedicine did not reach its full potential until the COVID-19 pandemic spurred its unparalleled adoption. This sudden shift in the setting of healthcare delivery raises questions regarding possible changes in clinical decision-making. Using a unique set of patient-provider encounter data from the U.S. in 2020 and 2021, we examine the effect of telemedicine on antibiotic prescription errors for urinary tract infections. After accounting for potential endogeneity issues using provider fixed effects and an instrumental variable approach, we find a significantly lower likelihood of prescription errors with telemedicine relative to in-person encounters. We also find heterogeneous effects by a provider's patient volume and the patient-provider relationship.