ABSTRACT
While thousands of new mobile applications (i.e., apps) are being added to the major app markets daily, only a small portion of them attain their financial goals and survive in these competitive marketplaces. A key to the quick growth and success of relatively less popular apps is that they should make their way to the limited list of apps recommended to users of already popular apps;however, the focus of the current literature on consumers has created a void of design principles for app developers. In this study, employing a predictive network analytics approach combined with deep learning-based natural language processing and explainable artificial intelligence techniques, we shift the focus from consumers and propose a developer-oriented recommender model. We employ a set of app-specific and network-driven variables to present a novel approach for predicting potential recommendation relationships among apps, which enables app developers and marketers to characterize and target appropriate consumers. We validate the proposed model using a large (>23,000), longitudinal dataset of medical apps collected from the iOS App Store at two time points. From a total of 10,234 network links (rec-ommendations) formed between the two data collection points, the proposed approach was able to correctly predict 8,780 links (i.e., 85.8 %). We perform Shapley Additive exPlanation (SHAP) analysis to identify the most important determinants of link formations and provide insights for the app developers about the factors and design principles they can incorporate into their development process to maximize the chances of success for their apps.
ABSTRACT
Timely decision-making in national and global health emergencies such as pandemics is critically important from various aspects. Especially, early identification of risk factors of contagious viral diseases can lead to efficient management of limited healthcare resources and saving lives by prioritizing at-risk patients. In this study, we propose a hybrid artificial intelligence (AI) framework to identify major chronic risk factors of novel, contagious diseases as early as possible at the time of pandemics. The proposed framework combines evolutionary search algorithms with machine learning and the novel explanatory AI (XAI) methods to detect the most critical risk factors, use them to predict patients at high risk of mortality, and analyze the risk factors at the individual level for each high-risk patient. The proposed framework was validated using data from a repository of electronic health records of early COVID-19 patients in the US. A chronological analysis of the chronic risk factors identified using our proposed approach revealed that those factors could have been identified months before they were determined by clinical studies and/or announced by the United States health officials.Copyright © 2022 The Author(s)
ABSTRACT
Based on a survey of undergraduate business students at a private Midwestern university in the United States, we found that the abrupt mid-semester transition from campus learning to at-home online learning due to the coronavirus disease of 2019 (COVID-19) pandemic led to an unexpected challenge for students. Students reported that stay-at-home learning eroded support for their student role while also creating conflicts between the student role and other competing roles, such as child, sibling, or supplemental wage earner. As a result, they significantly lacked motivation to complete schoolwork during stay-at-home orders. Using a framework rooted in role identity theory, we analyze this role erosion and role conflict. Based on that analysis, we suggest potential actions for faculty to mitigate the adverse impact that this role erosion/conflict has on learning and, thus, bolster the student role while simultaneously reducing conflict between the student role and other competing roles. As we brace for multiple semesters of teaching during the COVID-19 pandemic, such efforts to facilitate positive stay-at-home learning experiences for our students will contribute to determining our academic success and our educational institutions' economic viability.