Jae-Hyeon Ahn

With the rapid expansion of online food delivery (OFD) platforms, subscription models have emerged as a strategic tool to retain users and shape consumption behavior. Despite their growing prevalence, the broader impacts of these subscriptions—both within the OFD sector and across downstream consumer activities—remain underexplored. Leveraging a difference-in-differences approach and a large-scale individual-level transaction data set, we identify a significant shift in consumption from a competing platform to the focal platform following the introduction of a subscription service. This shift did not result in cannibalization; instead, overall food consumption within the OFD market increased. Geospatial analysis shows that subscribers were more likely to engage in offline spillovers, including increased local dining and activity in nearby noncompeting economic sectors. These patterns suggest that subscriptions in physical services, like OFD, not only reshape digital engagement, but also, they influence consumer mobility and stimulate localized economic ecosystems. Mechanistic testing supports the interpretation that these effects are driven by increased variety-seeking behavior for offline restaurants that predominantly serve non-OFD cuisines within a proximal location, suggesting that such behavior is constrained by transportation costs. Overall, this study offers novel insights into how subscription models in physical service platforms can expand market demand, foster crosschannel engagement, and generate positive spillovers in adjacent sectors. The findings offer actionable guidance for platform designers and policymakers to enhance consumer welfare and support local economic vitality. They also provide new evidence for restaurants that additional platform usage costs may be offset by latent economic benefits from heightened consumer engagement. This paper was accepted by D. J. Wu, information systems. Funding: F. J. Costello acknowledges funding by Fundação para a Ciência e a Tecnologia (UID/00124/2025, UID/ PRR/00124/2025, Nova School of Business and Economics DOI: https://doi.org/10.54499/UID/00124/2025 ), UID/PRR2/00124/2025: DOI: https://doi.org/10.54499/UID/PRR2/00124/2025 and LISBOA2030 (DataLab2030 - LISBOA2030-FEDER-01314200). Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2025.00954 .

Conventional online checkout in e-commerce requires consumers to complete multiple procedural steps, including account creation, shipping and payment information entry, and final confirmation, creating checkout-stage friction that can prevent intended purchases from becoming completed transactions. Third-party checkout infrastructure (TPCI) reduces this bottleneck by allowing consumers to complete purchases using credentials pre-stored with an external provider. For retailers, however, this convenience creates a strategic tension, since native checkout also serves as a gateway to retailer account creation. By compressing this process, TPCI may recover lost transactions while weakening incentives to enter the retailer’s own membership system, a key channel for direct customer relationships and retailer-controlled customer information. Using a staggered difference-in-differences design with monthly transaction data from 110 South Korean SME apparel retailers, we examine whether TPCI adoption improves transaction realization and whether it affects retailer-controlled enrollment. TPCI significantly increases orders, units sold, and net units sold, with gains concentrated among customers without retailer accounts; registered customers show no robust response. Monthly new membership signups do not meaningfully decline after adoption, and sales become less concentrated across a broader product range, primarily among unregistered customers. Drawing on technology affordance theory, we interpret these findings as evidence that TPCI expands a low-friction transactional affordance for previously constrained customers without substituting for the relational affordance of retailer membership. More broadly, externally supplied checkout infrastructure can produce functionally selective consequences within retailer-owned e-commerce systems, recovering transactions and broadening realized sales where native friction was greatest while leaving adjacent retailer-controlled outcomes comparatively stable.

With the rapid expansion of online food delivery (OFD) platforms, subscription models have emerged as a strategic tool to retain users and shape consumption behavior. Despite their growing prevalence, the broader impacts of these subscriptions—both within the OFD sector and across downstream consumer activities—remain underexplored. Leveraging a Difference-in-Differences (DID) approach and a large-scale individual-level transaction dataset, we identify a significant shift in consumption from a competing platform to the focal platform following the introduction of a subscription service. This shift did not result in cannibalization; instead, overall food consumption within the OFD market increased. Geospatial analysis shows subscribers were more likely to engage in offline spillovers, including increased local dining and activity in nearby non-competing economic sectors. These patterns suggest that subscriptions in physical services like OFD not only reshape digital engagement but also influence consumer mobility and stimulate localized economic ecosystems. Mechanistic testing supports the interpretation that these effects are driven by increased variety-seeking behavior for offline restaurants that predominantly serve non-OFD cuisines within a proximal location suggesting such behavior is constrained by transportation costs. Overall, this study offers novel insights into how subscription models in physical service platforms can expand market demand, foster cross-channel engagement, and generate positive spillovers in adjacent sectors. The findings offer actionable guidance for platform designers and policymakers to enhance consumer welfare and support local economic vitality. They also provide new evidence for restaurants that additional platform usage costs may be offset by latent economic benefits from heightened consumer engagement.

Smart speakers enable communication through a voice interface and are among the fastest growing consumer technologies globally; they provide a new and innovative medium for the delivery of interactive advertisements and the sale of products, known as “conversational commerce.” However, despite the promising potential of the use of smart speakers as a new advertising channel, little is known about the effectiveness of advertisements made through this medium. Therefore, this article investigates how the distinctive features of smart speakers, namely, interactivity, talker variability, and contextual relevance, influence the effectiveness of advertisements. Through two experiments, we assess the recognition of advertisements for the measurement of advertisements effectiveness through smart speakers. The results of experiment 1 show that the brand and product recognition of advertisements in cases where smart speaker users interact with the advertisements is higher as compared to that of non-interactive advertisements. Experiment 2 further shows that contextual relevance increases the brand and product recognition of advertisement, while talker variability does not affect it. Thus, our findings reveal the nature of the voice interface and provide insights for effective business strategies for advertisements in conversational commerce.

Background: The COVID-19 pandemic has placed an unprecedented burden on health care systems. Objective: We aimed to effectively triage COVID-19 patients within situations of limited data availability and explore optimal thresholds to minimize mortality rates while maintaining health care system capacity. Methods: A nationwide sample of 5601 patients confirmed with COVID-19 until April 2020 was retrospectively reviewed. Extreme gradient boosting (XGBoost) and logistic regression analysis were used to develop prediction models for the maximum clinical severity during hospitalization, classified according to the World Health Organization Ordinal Scale for Clinical Improvement (OSCI). The recursive feature elimination technique was used to evaluate the maintenance of model performance when clinical and laboratory variables were eliminated. Using populations based on hypothetical patient influx scenarios, discrete-event simulation was performed to find an optimal threshold within limited resource environments that minimizes mortality rates. Results: The cross-validated area under the receiver operating characteristic curve (AUROC) of the baseline XGBoost model that utilized all 37 variables was 0.965 for OSCI ≥6. Compared to the baseline model’s performance, the AUROC of the feature-eliminated model that utilized 17 variables was maintained at 0.963 with statistical insignificance. Optimal thresholds were found to minimize mortality rates in a hypothetical patient influx scenario. The benefit of utilizing an optimal triage threshold was clear, reducing mortality up to 18.1%, compared with the conventional Youden index. Conclusions: Our adaptive triage model and its threshold optimization capability revealed that COVID-19 management can be achieved via the cooperation of both the medical and health care management sectors for maximum treatment efficacy. The model is available online for clinical implementation.

BACKGROUND The COVID-19 pandemic has placed an unprecedented burden on health care systems. OBJECTIVE We aimed to effectively triage COVID-19 patients within situations of limited data availability and explore optimal thresholds to minimize mortality rates while maintaining health care system capacity. METHODS A nationwide sample of 5601 patients confirmed with COVID-19 until April 2020 was retrospectively reviewed. Extreme gradient boosting (XGBoost) and logistic regression analysis were used to develop prediction models for the maximum clinical severity during hospitalization, classified according to the World Health Organization Ordinal Scale for Clinical Improvement (OSCI). The recursive feature elimination technique was used to evaluate the maintenance of model performance when clinical and laboratory variables were eliminated. Using populations based on hypothetical patient influx scenarios, discrete-event simulation was performed to find an optimal threshold within limited resource environments that minimizes mortality rates. RESULTS The cross-validated area under the receiver operating characteristic curve (AUROC) of the baseline XGBoost model that utilized all 37 variables was 0.965 for OSCI ≥6. Compared to the baseline model’s performance, the AUROC of the feature-eliminated model that utilized 17 variables was maintained at 0.963 with statistical insignificance. Optimal thresholds were found to minimize mortality rates in a hypothetical patient influx scenario. The benefit of utilizing an optimal triage threshold was clear, reducing mortality up to 18.1%, compared with the conventional Youden index. CONCLUSIONS Our adaptive triage model and its threshold optimization capability revealed that COVID-19 management can be achieved via the cooperation of both the medical and health care management sectors for maximum treatment efficacy. The model is available online for clinical implementation.

This study empirically investigates (1) how the sales distribution in the mobile commerce channel is different from the sales distribution in the traditional PC channel and (2) how mobile commerce channel adoption affects e-market users’ search intensity and their aggregate sales distribution. Our analysis shows that transactions in the mobile channel are more concentrated on “head” products compared with PC channel sales. However, it does not imply that the presence of the mobile channel concentrates the demand on head products in online markets, given that mobile channel users can purchase products in the PC channel as well. On the basis of the subsequent user-level analysis, we reveal that in the case of preference goods such as books, CDs, toys, and fashion items, mobile commerce channel adoption increased e-market users’ search activities and resulted in more “tail” product sales. For quality goods such as PCs, phones, cameras, and digital appliances, however, adoption intensified the search activities but resulted in more head product sales. Finally, for convenience goods such as home supplies and processed foods, adoption discouraged search activities and decreased the choice of tail products.

Background: Recent progress in genome data collection and analysis technologies has led to a surge of direct-to-consumer (DTC) genetic testing services. Owing to the clinical value and sensitivity of genomic data, as well as uncertainty and hearsay surrounding business practices of DTC genetic testing service providers, DTC genetic testing has faced significant criticism by researchers and practitioners. Research in this area has centered on ethical and legal implications of providing genetic tests directly to consumers, but we still lack a more profound understanding of how businesses in the DTC genetic testing markets work and provide value to different stakeholders. Objective: The aim of this study was to address the lack of knowledge concerning business models of DTC genetic testing services by systematically identifying the salient properties of various DTC genetic testing service business models as well as discerning dominant business models in the market. Methods: We employed a 3-phased research approach. In phase 1, we set up a database of 277 DTC genetic testing services. In phase 2, we drew on these data as well as conceptual models of DTC genetic testing services and iteratively developed a taxonomy of DTC genetic testing service business models. In phase 3, we used a 2-stage clustering method to cluster the 277 services that we identified during phase 1 and derived 6 dominant archetypes of DTC genetic testing service business models. Results: The contributions of this research are 2-fold. First, we provided a first of its kind, systematically developed taxonomy of DTC genetic testing service business models consisting of 15 dimensions in 4 categories. Each dimension comprises 2 to 5 characteristics and captures relevant aspects of DTC genetic testing service business models. Second, we derived 6 archetypes of DTC genetic testing service business models named as follows: (1) low-cost DTC genomics for enthusiasts, (2) high-privacy DTC genomics for enthusiasts, (3) specific information tests, (4) simple health tests, (5) basic low-value DTC genomics, and (6) comprehensive tests and low data processing. Conclusions: Our analysis paints a much more complex business landscape in the DTC genetic testing market than previously anticipated. This calls for further research on business models and their effects that underlie DTC genetic testing services and invites specific regulatory interventions to protect consumers and level the playing field.

BACKGROUND Recent progress in genome data collection and analysis technologies has led to a surge of direct-to-consumer (DTC) genetic testing services. Owing to the clinical value and sensitivity of genomic data, as well as uncertainty and hearsay surrounding business practices of DTC genetic testing service providers, DTC genetic testing has faced significant criticism by researchers and practitioners. Research in this area has centered on ethical and legal implications of providing genetic tests directly to consumers, but we still lack a more profound understanding of how businesses in the DTC genetic testing markets work and provide value to different stakeholders. OBJECTIVE The aim of this study was to address the lack of knowledge concerning business models of DTC genetic testing services by systematically identifying the salient properties of various DTC genetic testing service business models as well as discerning dominant business models in the market. METHODS We employed a 3-phased research approach. In phase 1, we set up a database of 277 DTC genetic testing services. In phase 2, we drew on these data as well as conceptual models of DTC genetic testing services and iteratively developed a taxonomy of DTC genetic testing service business models. In phase 3, we used a 2-stage clustering method to cluster the 277 services that we identified during phase 1 and derived 6 dominant archetypes of DTC genetic testing service business models. RESULTS The contributions of this research are 2-fold. First, we provided a first of its kind, systematically developed taxonomy of DTC genetic testing service business models consisting of 15 dimensions in 4 categories. Each dimension comprises 2 to 5 characteristics and captures relevant aspects of DTC genetic testing service business models. Second, we derived 6 archetypes of DTC genetic testing service business models named as follows: (1) low-cost DTC genomics for enthusiasts, (2) high-privacy DTC genomics for enthusiasts, (3) specific information tests, (4) simple health tests, (5) basic low-value DTC genomics, and (6) comprehensive tests and low data processing. CONCLUSIONS Our analysis paints a much more complex business landscape in the DTC genetic testing market than previously anticipated. This calls for further research on business models and their effects that underlie DTC genetic testing services and invites specific regulatory interventions to protect consumers and level the playing field.