Digital Transformations of Business Operations

Permanent URI for this collectionhttps://hdl.handle.net/10125/112539

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Now showing 1 - 11 of 11

Implementing Cooperative IoT Systems – A Product Development Method
(2026-01-06) Kurrle, Sven
The Internet of Things (IoT) is increasingly capable of enabling cooperative products built by more than one company. Such cooperative IoT systems are developed by orchestrating existing products to provide novel value. This paper presents a novel development method for implementing such systems. The method was developed based on a 41-month longitudinal study in which teams from three large companies implemented a cooperative IoT system. The method identifies three phases that design, build, and evolve the IoT system. Three key factors observed in the case study are highlighted. This method provides a practical guide for research into IoT system development and practitioners seeking to implement such systems.
Media Platforms and Technology Disruption: Pricing in the Digital Age
(2026-01-06) Garg, Anurag; Cho, Soohyun; Bandyopadhyay, Subhajyoti
This research analyzes competition between streaming media platforms (Content Providers, CPs) employing different revenue models: ad-supported versus personalized, ad-free subscriptions. We uniquely model consumer heterogeneity across two dimensions: content preference and ad tolerance, departing from prior literature's uniform ad disutility. Our game-theoretical analysis determines optimal pricing strategies under varying market conditions, showing how CPs capture distinct market segments. Key findings reveal that high ad revenue per user (ARPU) drives CPs to prioritize ad-supported services, potentially marginalizing premium offerings. Conversely, strong consumer loyalty to content preferences enables CPs to raise prices for ad-free services. The study offers theoretical insights and actionable advice for navigating the evolving SVOD landscape.
How Work Performance Shapes Giving in Digital Labor Markets
(2026-01-06) Tan, Xue (Jane); Gan, Rowena; Lee, Kyunghee
Most charitable gifts come from wages, yet scholarship rarely asks whether the way those wages are earned shapes the way they are donated. We study this question with a two-stage approach. First, we develop an analytical model in which work effort yields both disposable income and identity feedback, creating two routes from performance to generosity: a budget channel (more resources) and a feedback channel (enhanced self-regard). Second, we empirically test the model's predictions with longitudinal, individual-level data from Gitcoin, a blockchain platform that pairs crowdsourcing and crowdfunding. Gitcoin timestamps every work task and donation on-chain, enabling us to track earnings and donations for the same contributors over time. Our results show that positive work feedback leads to earlier subsequent donations, though it does not necessarily increase donation amounts. Furthermore, a higher balance in the work income account raises donation amounts but does not always lead to earlier donations. The findings extend economic models of giving by endogenizing income generation, carrying digital-citizenship insights, and demonstrating the research potential of transparent digital labor ledgers.
How Does Authenticity Observed from Service Provider–Consumer Interactions Affect Consumer Engagement and Firm Performance? Evidence from a Field Experiment in a Restaurant Chain
(2026-01-06) Zhang, Yixuan; Yan, Xiangbin; Gu, Bin
Digital platforms make many firm–customer interactions publicly observable, yet the impact of the authenticity conveyed by service providers in these interactions on future consumers remains unclear. We explore how service provider authenticity, as observed from a third-party perspective, affects subsequent consumer engagement and firm performance. Collaborating with a chain restaurant firm, we employ a mixed-method design combining secondary data analysis and a field experiment. Results indicate that authenticity significantly increases visits to the ordering page, orders, and sales. Guided by interactional justice and attribution theories, we further find that these effects are pronounced in service-failure contexts. Also, the impact of authenticity intensifies when observers witness firm-induced failures and disappears when failures are attributed externally. Our research contributes to the authenticity literature by introducing a third-party observational perspective and revealing the economic value of authenticity on digital service platforms. Practically, our findings provide implications for firms to optimize online operations.
Information Technologies and the Search for Top Talent in Competitive Job Markets
(2026-01-06) Tilson, Vera; Jing, Xi; Seidmann, Abraham; Gu, Bin
The search for highly talented professional employees has been dramatically reshaped by new information technologies. On the one hand, the rise of both dedicated and general-purpose job platforms has significantly reduced the cost and effort for applicants to apply to many more positions. In addition, the introduction of powerful LLM-based tools enables applicants to submit more polished and better-targeted materials, further lowering the signal-to-noise ratio in the application pool. On the other hand, recruiters now face an overwhelming number of applications per opening and have turned to more sophisticated selection rules and AI tools to identify candidates worth interviewing. Because application materials are not perfectly correlated with an applicant’s true inherent quality or fit, and because applicants often conceal their job preferences, recruiters continue to rely on personal interviews—both to better gauge candidate quality and to market their positions. Overall, while applicants can now submit large batches of applications easily and at low cost, recruiters, flooded with submissions, find that the search process has become increasingly expensive and time-consuming. This study situates the U.S. National Resident Matching Program (NRMP) within the broader problem of hiring in markets where specialized platforms dramatically increase application volume. As in other professional labor markets, residency programs compete for applicants in an environment where individuals can cheaply submit large numbers of applications, generating excess competition and noise. We focus on program-level strategies for managing the transition from applications to interviews—through adjustments in interview volume, screening accuracy, or interview cutoffs. Our analysis considers both symmetric settings, where programs are identical in applicant perceptions, and asymmetric settings, where one program is uniformly more desirable. A key and somewhat counterintuitive result is that additional effort by one program does not necessarily disadvantage its competitors and can, in some cases, improve overall match outcomes.
Does Free Consultation Promotion Affect Paid Consultations? An Empirical Investigation of Online Medical Consultations
(2026-01-06) Zhang, Ran Alan; Wang, Yunting; Guo, Xitong; Kumar, Subodha
Recently, online healthcare platforms have started offering free consultation services for patients and have launched promotions to encourage physicians to engage in free consultations. However, it is unclear whether and how these promotions may spill over and affect paid consultations, which is the primary form of OMCs and the major profit for OHPs. We fill this gap and provide practical suggestions by studying the spillover effect of platform-initiated promotion of free consultations on subsequent paid consultations. Exploiting an OHP-initiated event, we draw causal inferences by adopting a staggered difference-in-difference approach combined with matching techniques. We find that the free-consultation promotion has a positive spillover effect on the volume of subsequent paid consultations. Our mechanism analyses suggest that this result may stem from patient-side changes through two pathways: a direct freemium effect and an indirect, mediating awareness effect. Our findings provide all stakeholders with a deeper understanding of the impact of free consultations on OHPs and insights into promoting such services.
Does Enforcement of Drug Regimen Reviews Improve Medication Safety and Management in Nursing Homes? An Analysis of the CMS Drug Regimen Review Policy
(2026-01-06) Wan, Fang
In 2019, the Centers for Medicare & Medicaid Services (CMS) implemented a mandatory comprehensive drug regimen review (DRR) policy in nursing homes (NHs) to address medication-related issues that significantly contribute to morbidity, mortality, hospital readmissions, and escalating healthcare costs among residents. Prior to the policy's implementation, nursing homes frequently faced deficiency citations for medication errors and inadequate drug management practices. This study assesses the policy's impact by comparing trends in deficiency citations before and after the policy enactment, with a particular focus on overall citation prevalence and those specifically related to medication management. Findings from this research offer valuable evidence regarding the effectiveness of the CMS DRR policy in improving medication safety and the quality of care in nursing homes.
Using RFID Data to Improve the Identification of Abandonment Behavior in an Emergency Department: Clinical Policy Implications
(2026-01-06) Ravid, Yaniv; Ibarhim, Rouba; Hu, Junqi; Pasupathy, Kal; Nestler, David; Ernste, Vickie; Sarhangian, Vahid; Afèche, Philipp
Identifying which Emergency Department (ED) patients are likely to leave without being seen (LWBS) could enable interventions that reduce LWBS rates. Machine Learning (ML) models that updated these predictions as patients wait were developed and validated, to correctly identify more patients who LWBS. Using a dataset of 150,959 patient visits to the ED of an academic medical campus, two types of classification models were developed: (1) a static model that uses patient and ED census information at the time of arrival to predict the risk to LWBS; and (2) a time-dependent model that updates the predictions based on new information after 30 minutes for patients who are still waiting in the ED. Preliminary results show that the time-dependent model reduces the number of missed LWBS cases by approximately 50% as compared to the static model, without incurring any additional false-positives.
Corporate Default Prediction Through Text Mining: Integrating Event, Sentiment, and Network Analyses
(2026-01-06) Yao, Xuan; Su, Yating; Tan, Tianhui; Huang, Ke Wei
The importance of textual information in corporate credit risk management is increasingly recognized. While most studies focus on the direct analysis for assessing corporate credit risk, they often overlook the potential impact of inter-company relationships on the likelihood of default. This study, focusing on both intrinsic information about companies themselves and relational information within company networks, explores the potential of advanced text-mining techniques for predicting corporate defaults. We integrate default event extraction, credit sentiment analysis, and relation analysis via co-mention networks using public news on US-listed oil companies between 2014 and 2016. We aim to demonstrate how these advanced text-derived features enhance default prediction during industry upheaval. Our findings reveal that credit sentiment emerges as a crucial predictor of default, alongside network degree and transitivity. High-risk labelled companies are more likely to default than others. Moreover, exposure to media, regardless of being positive or negative, may increase the likelihood of both default and other corporate exits, primarily mergers and acquisitions. This study emphasizes the transformative impact of text analysis on traditional credit risk assessment practices and underscores the value of relational information between companies for default prediction.
Design AI-Generated Summaries for Online Video Platforms: Evidence from a Field Experiment
(2026-01-06) Wan, Xiang (Shawn); Li, Allen; Ji, Ying; Gao, Chaoyue
AI-generated summaries condense lengthy videos into concise text, helping reduce information overload and guide user attention. This research designs two summarization strategies for online videos: information-extractive AI-generated summaries (IAIGS), which present fact-based recaps, and suspense-inducing AI-generated summaries (SAIGS), which withhold key details to spark curiosity. To assess their impact on video consumption, we conducted a randomized field experiment on a video-sharing platform, focusing on two content genres: Science (instrumental needs), and Humanities & History (affective needs). Our results show that IAIGS reduced video views across both genres, driven by information substitution. In contrast, SAIGS have genre-specific effects: they increase engagement with Humanities & History content by sparking curiosity but decrease engagement with Science content by obstructing information-seeking. Overall, our study underscores that summary design shapes content consumption and that the effectiveness of AI-generated summaries depends on the dynamics of human–AI interaction. The findings provide practical insights for designing summaries to enhance user engagement and content consumption.
Introduction to the Minitrack on Digital Transformations of Business Operations
(2026-01-06) Zhang, Jie; Jiang, Yabing; Seidmann, Abraham

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