Digital Transformations of Business Operations

Permanent URI for this collectionhttps://hdl.handle.net/10125/107556

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Now showing 1 - 7 of 7

The Dynamic Integration of AI - Driven Telemedicine with In-Office Specialty Care
(2024-01-03) Baron, Opher; Chen, Fanying; Seidmann, Abraham
Nation-wide physician shortages and the rise of virtual access to healthcare have driven many organizations to seek innovative ways to integrate digital and automated care programs to serve their members better. We work closely with a large HMO and investigate the option of incorporating an independent third-party service that provides select patients rapid access to virtual Dermatological AI Systems. In this paper, we analyze the impact of this innovative hybrid service system on the waiting times for highly-demanded specialty medical care: here, the automated appointment scheduling system dynamically activates the virtual care only when the in-person service lines get overloaded beyond a predetermined threshold. Using extensive appointment field data, we show that our proposed hybrid service delivery policy significantly improves service quality - even when the virtual care channel serves only a tiny proportion of all patients.
Tele-Follow-Up and Outpatient Care
(2024-01-03) Sun, Shujing; Gu, Wei; Li, Meng
We examine the application of telemedicine for follow-up care (i.e., tele-follow-up). By collaborating with a large Asian hospital that sequentially adopted the tele-follow-up service in different departments, we leverage the difference-in-differences design and find that the adoption of telemedicine significantly increases the follow-up volume by 54%. Moreover, telemedicine generates positive spillover effects on onsite care provision, with onsite follow-up visits increasing by 10.7% and onsite initial visits increasing by 5.7%. The mechanism test shows that the treatment effect is heterogeneous by patients' cost sensitivity to onsite follow-up care. Finally, we show that tele-follow-up improves patient care quality, as evidenced by a significant reduction in the readmission rate, which reinforces the value of tele-follow-up applications.
Information Technology and Human Resource Management: Revisiting the Past to Inform the Future
(2024-01-03) Johnson, Richard; Kuhn, Kristine
Despite near universal adoption by medium and large organizations, evidence regarding the effectiveness of information technology (IT) in human resource management (HRM) is mixed. The present study examines why these potential inconsistencies might exist and provides remedies to overcome these potential inconsistencies. To do this, we first develop a framework that categorizes outcomes associated with the use of IT in HRM by the level of organizational functioning (e.g., operational, managerial, and strategic) as well as the functional are of HRM of interest to the researcher. Using this framework, we classify the dependent variables from 332 studies and identify three potential explanations for the seemingly inconsistent findings. We then present three potential remedies to advance research in this domain. Insights from this research should provide suggestions for research that will help develop a richer understanding of how IT supports HRM.
Effect of New Goal Disclosure on Service Employee’s Effort Allocation: A Quasi-Experiment Study
(2024-01-03) Zhu, Yongmin; Zhang, Yueyue; Zhang, Cheng
Employees play a key role in implementing firms’ service strategies with new and established customers. However, few empirical studies have investigated whether and how service employees voluntarily adapt their behaviors in alignment with their organization’s customer service strategies. By applying organizational learning theory, this study hypothesizes and investigates how goal disclosure in a firm’s work system influences service employees’ effort allocation between new and established customers. The results suggest that service employees voluntarily adjust their effort allocation in response to the new goal. Furthermore, the adjustment is amplified for service employees with a more diversified customer portfolio and higher past performance. This study supports that goal disclosure per se, even in the absence of monetary incentives, can motivate service employees’ effort allocation. Important contributions and implications are also discussed in the paper.
Solving trade-offs between resilience and sustainability in supply chains: a case study on real-time tracking technologies
(2024-01-03) Herbe, Amandine
Both resilience and sustainability are crucial objectives that today’s supply chains need to pursue. However, the intersection between both is unclear, and some situations lead to trade-offs. We conduct a case study on the use of real-time tracking technologies to analyze these tensions and suggest solutions to overcome them. This study explores the root causes of expedited shipments as a result of a trade-off between sustainability and resilience using qualitative empirical data from a case company. We evaluate how a real-time tracking solution (RLTS) implementation contributes to resolving this trade-off by considering the underlying tension factors. From technical, process, data, capabilities, and user perspectives, we identify the primary value drivers of RLTS for sustainable and resilient supply chains (SRSC). To enable practitioners to fully leverage the benefits of increased transparency, we generalize our findings to IT artifacts providing supply chain transparency and position RLTS within the SRSC academic discussion.
The Effect of Interpretable Artificial Intelligence on Repeated Managerial Decision-Making under Uncertainty
(2024-01-03) Altintas, Onur; Seidmann, Abraham; Gu, Bin; Mažar, Nina
Business decisions involving investments, healthcare, and supply chains are often made in uncertain environments. At the same time, despite being optimal initially, such choices may seem incorrect in hindsight, which may explain why decision-makers hesitate to use AI algorithms under high uncertainty. While some studies suggest that making AI and ML applications more understandable can boost their adoption and trust, this hasn’t been examined in uncertain conditions where decision-makers must make repetitive business decisions. Our study addresses this issue empirically by analyzing how different interpretability approaches affect AI adoption and trust under varying levels of uncertainty. Surprisingly, we find that providing interpretability does not necessarily increase AI adoption. In some cases, it may even reduce AI adoption. Interestingly, even though AI adoption was higher, trust in the AI recommendations was significantly lower in high uncertainty compared to low uncertainty across all interpretability types. The evidence is clear that showing the cumulative monetary performance of AI to the users as a benchmark, side by side with their own monetary performance, enhances trust in the AI recommendations.
Introduction to the Minitrack on Digital Transformations of Business Operations
(2024-01-03) Jiang, Yabing; Zhang, Jie; Seidmann, Abraham

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