Francis Joseph Costello

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 .

This study presents a novel approach to examining the psychological impact of emerging technologies through the development of a Hype Cycle Model (HCM), utilizing sentiment analysis and topic modeling. Focusing on electric vehicles, we investigate how public sentiment—captured via social media comments—reflects the psychological effects of technology adoption and hype. Our model integrates both qualitative and quantitative analyses, utilizing sentiment scoring and topic modeling to explore thematic psychological trends. An analysis of approximately 43,000 social media comments on electric vehicles demonstrated that the integration of expert knowledge with public sentiment provides a comprehensive understanding of technology hype dynamics. The results revealed that sentiment analysis enables real-time tracking of emotional responses to emerging technologies, while Correlated Topic Modeling (CTM) offers contextual insights into the positioning of technologies within the HCM. These findings demonstrate that understanding public sentiment towards emerging technologies can provide valuable insights for both organizations and policymakers in technology forecasting and adoption planning. Our approach transforms the traditional black box implementation by Gartner Inc. into a transparent framework that illuminates the psychological underpinnings of technology hype, revealing how collective excitement, expectations, and emotional responses shape the trajectory of emerging technology adoption.

Purpose: This study examines how structured procedures and psychological safety can enhance six sigma processes’ effectiveness and foster individual creativity within an organization.Design/methodology/approach: This study employs a novel methodological approach, combining structural equation modeling (SEM) and multi-value qualitative comparative analysis (mvQCA).This combination gave us a unique lens through which to view the complex connections between the constructs that were the subject of the investigation.Findings: The results substantiate that a well-structured six sigma approach, paired with a psychologically safe environment, fosters knowledge exploration and exploitation as well as triggering conducive learning behaviors. These elements, in turn, augment individual creativity, which is found to be instrumental in boosting six sigma performance.Originality/value: This study is one of the first to combine mvQCA and SEM to investigate the complex interplay between structured methods, psychological safety and individual creativity in enhancing six sigma performance. This research highlights the importance of fostering creativity in strategic management processes and the unrealized potential of combining these methods.

The goal of the current study was to provide information on the potential of neuroscience mining (NSM) for comprehending NeuroIS paradigms. NSM is an interdisciplinary field that combines neuroscience and business mining, which is the application of big data analytics, computational social science, and other fields to business problems. Therefore, NSM makes it possible to apply predictive models to NeuroIS datasets, such as machine learning and deep learning, to find intricate patterns that are hidden by conventional regression-based analysis. We predicted 28 individual EEG power spectra separated brainwave data using a Random Forest (RF) model. Next, we used NSM to precisely predict how consumers would perceive a product online, depending on whether a light or dark user interface (UI) mode was being used. The model was then used to extract more precise results that could not be obtained using more conventional linear-based analytical models using sensitivity analysis. The benefits of using NSM in NeuroIS research are as follows: (1) it can relieve the burden of the three-horned dilemma described by Runkel and McGrath; (2) it can enable more temporal data to be directly analyzed on the target variables; and (3) sensitivity analysis can be performed on a condition/individual basis, strengthening the rigor of findings by reducing sample bias that can be lost in grand averaging of data when analyzed with methods like GLM.

Several countries nowadays are facing a tough social challenge caused by the aging population. This public health issue continues to impose strain on clinical healthcare, such as the need to prevent terminal patients’ pressure ulcers. Provocative approaches to resolve this issue include health information technology (HIT). In this regard, this paper explores one technological solution based on a smart medical bed (SMB). By integrating a convolutional neural network (CNN) and long-short term memory (LSTM) model, we found this model enhanced performance compared to prior solutions. Further, we provide a fuzzy inferred solution that can control our proposed proprietary automated SMB layout to optimize patients’ posture and mitigate pressure ulcers. Therefore, our proposed SMB can allow autonomous care to be given, helping prevent medical complications when lying down for a long time. Our proposed SMB also helps reduce the burden on primary caregivers in fighting against staff shortages due to public health issues such as the increasing aging population.

It has been reported repeatedly that depression in middle-aged people may cause serious ramifications in public health. However, previous studies on this important research topic have focused on utilizing either traditional statistical methods (i.e., logistic regressions) or black-or-gray artificial intelligence (AI) methods (i.e., neural network, Support Vector Machine (SVM), ensemble). Previous studies lack suggesting more decision-maker-friendly methods, which need to produce clear interpretable results with information on cause and effect. For the sake of improving the quality of decisions of healthcare decision-makers, public health issues require identification of cause and effect information for any type of strategic healthcare initiative. In this sense, this paper proposes a novel approach to identify the main causes of depression in middle-aged people in Korea. The proposed method is the Sons and Spouses Bayesian network model, which is an extended version of conventional TAN (Tree-Augmented Naive Bayesian Network). The target dataset is a longitudinal dataset employed from the Korea National Health and Nutrition Examination Survey (KNHANES) database with a sample size of 8580. After developing the proposed Sons and Spouses Bayesian network model, we found thirteen main causes leading to depression. Then, genetic optimization was executed to reveal the most probable cause of depression in middle-aged people that would provide practical implications to field practitioners. Therefore, our proposed method can help healthcare decision-makers comprehend changes in depression status by employing what-if queries towards a target individual.

본 연구는 신경생리학적 분석의 관점에서 창의성의 기존 문헌에 공헌하는 것을 목표로 한다. 특히 감정이 비즈니스 문제 해결 창의성(BPSC: Business Problem-Solving Creativity)에 어느 정도의 영향을 미치는지를 뇌파(EEG) 분석을 통하여 살펴보았다. 본 연구에서 적용한 실험에서는 실험참가자로 하여금 경영정보분야에서 널리 사용되는 인지지도(cognitive map)에 기반한 비즈니스 문제를 해결하도록 하고 그 결과를 평가함으로써 비즈니스 문제해결 창의성을 측정하였다. 총 34명의 참가자를 대상으로 하였고 EEG 분석을 통해 실험자료를 분석한 결과 긍정적인 감정에서보다 부정적인 감정하에서 비즈니스 문제해결 창의성이 통계적으로 유의미하게 증가하는 것을 확인할 수 있었다. 본 연구는 경영정보분야 의사결정기법을 적용한 BPSC를 뇌파분석을 통하여 실증적으로 분석할 수 있음을 보여주었다는 면에서 기존연구에 기여한다.

More and more these days, airport security is having to adapt to a greater number of passengers while keeping within finer margins of precision when it comes to clearing passengers for security clearance. Therefore, analyzing potential upgrades in the security process and adopting them in a way that does not impact passenger experience has become a key activity for airport managers. One viable technological solution that is not only effective but also efficient is biometric security. This technology allows for passengers to safely clear security based on their unique biometric features. Despite its promise, airports and passengers alike are slow to adopt its use. Additionally, there were few studies that revealed insights into passengers’ intentions toward repeat use of biometric security. Hence, in our novel attempts to uncover the underlying reasons, we implemented a study on passengers’ initial and repeat usage intention based on perceived benefits and risks of biometric security technology. Based on 327 survey responses, we employed the use of SEM to extract first- and second-order constructs before evaluating our hypotheses on a finally accepted model. To be aligned with the novel attempt of this research, the results showed that both the perceived benefits and risks have a significant impact on passenger’s initial and repeat use intention of biometric security. Therefore, for all practitioners engaged in sustainable airport management, strong consideration from this research should help in creating value for passengers while mitigating the risks of adopting biometric security within airport security settings.