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BackgroundOnline health care consultation has become increasingly popular and is considered a potential solution to health care resource shortages and inefficient resource distribution. However, many online medical consultation platforms are struggling to attract and retain patients who are willing to pay, and health care providers on the platform have the additional challenge of standing out in a crowd of physicians who can provide comparable services. ObjectiveThis study used machine learning (ML) approaches to mine massive service data to (1) identify the important features that are associated with patient payment, as opposed to free trial–only appointments; (2) explore the relative importance of these features; and (3) understand how these features interact, linearly or nonlinearly, in relation to payment. MethodsThe dataset is from the largest China-based online medical consultation platform, which covers 1,582,564 consultation records between patient-physician pairs from 2009 to 2018. ML techniques (ie, hyperparameter tuning, model training, and validation) were applied with four classifiers—logistic regression, decision tree (DT), random forest, and gradient boost—to identify the most important features and their relative importance for predicting paid vs free-only appointments. ResultsAfter applying the ML feature selection procedures, we identified 11 key features on the platform, which are potentially useful to predict payment. For the binary ML classification task (paid vs free services), the 11 features as a whole system achieved very good prediction performance across all four classifiers. DT analysis further identified five distinct subgroups of patients delineated by five top-ranked features: previous offline connection, total dialog, physician response rate, patient privacy concern, and social return. These subgroups interact with the physician differently, resulting in different payment outcomes. ConclusionsThe results show that, compared with features related to physician reputation, service-related features, such as service delivery quality (eg, consultation dialog intensity and physician response rate), patient source (eg, online vs offline returning patients), and patient involvement (eg, provide social returns and reveal previous treatment), appear to contribute more to the patient’s payment decision. Promoting multiple timely responses in patient-provider interactions is essential to encourage payment.

Background: The number of accidents due to distracted pedestrian is on the rise and many governments and institutions are enacting public policies which restrict texting while walking. However, pedestrians do more than just texting when they use their mobile devices on the go. Objective: Exploring pedestrian multitasking, this paper aims to examine the effects of mobile device task type on pedestrian performance outcomes. Method: We performed two studies in lab simulations where 78 participants were asked to perform different tasks on a mobile device (playing a game, reading, writing an email, texting one person, group texting) while performing a pedestrian visual discrimination task while either standing or walking on a treadmill. Behavioral performance as well as neurophysiological data are collected. Results: Results show that compared to a no-phone control, multitasking with any of the tasks on a mobile device leads to poor performance on a pedestrian visual discrimination task. Playing a game is the most cognitively demanding task and leads to the greatest performance degradation. Conclusion: Our studies show that multitasking with a mobile device has the potential to negatively impact pedestrian safety, regardless of task type. However, the impacts of different mobile device tasks are not all equivalent. More research is needed to tease out the different effects of these various tasks and to design mobile applications which effectively and safely capture pedestrians' attention. Application: Public policy, infrastructure, and smart technologies can be used to mitigate the negative effects of mobile multitasking. A more thorough understanding of mobile device task-specific factors at play can help tailor these counter-measures to better aid distracted pedestrians. [ABSTRACT FROM AUTHOR]

Deception is an unfortunate staple in group work. Guarding against team members’ deceptive tactics and alternative agendas is difficult and may seem even more difficult in technology-driven business environments that have made multitasking during teamwork increasingly commonplace. This research develops a foundation for a nuanced theoretical understanding of deception detection under these conditions. The intersection of information technology multitasking and deception detection theories is shown to produce various and sometimes competing ideas about how this type of multitasking might affect truthfulness assessments in real-time teamwork. A laboratory study involving a collaborative game helped evaluate the different ideas using manipulated deception and multitasking behaviors in a real-time, virtual group environment. The results provide evidence that information multitasking can actually improve deception detection, likely because multitaskers engage less in the team conversation, making themselves less manipulable. As understanding of multitasking benefits increases, managers and designers can incorporate effective multitasking into collaborative processes. [ABSTRACT FROM AUTHOR]

Self-monitoring is a strategy that patients use to manage their chronic disease and chronic disease risk factors. Technological advances such as mobile apps, web-based tracking programs, sensing devices, wearable technologies, and insideable devices enable IT-based self-monitoring (ITSM) for chronic disease management. Since ITSM is multidisciplinary in nature and our understanding is fragmented, a systematic examination of the literature is performed to build a holistic understanding of the phenomenon. We review 159 studies published in 108 journals and conferences between 2006 and 2017. By adapting affordance actualization theory, we develop an overarching framework to organize the existing literature on ITSM for chronic disease management. Four themes emerge: key ITSM functionalities that enable affordances; effects on ITSM system use; effects on the achievement of chronic care goals; and the role of intermediary outcomes. For each theme, we identify what is known, what is unknown, and opportunities for future research. We also discuss cross-theme opportunities for future research where more diverse theoretical perspectives can contribute to our understanding of the phenomenon. This work provides research directions for IS researchers studying ITSM for chronic disease self-management. [ABSTRACT FROM AUTHOR]

When teaching Information Systems (IS), one of the crucial objectives is to make students understand the practical aspects of the integration of IS in organizations. Over the last decades, several pedagogical approaches were introduced to more tightly bridge theory and practice, e.g., hands on exercises, simulations, real world projects, guest speakers, and case studies. In this paper, we introduce a pedagogical approach novel to IS which brings practice into the classroom, i.e., the live teaching case method. The live teaching case method is a hybrid between a guest speaker event and a teaching case. The live teaching case method is different from a written case as it is the animator who experienced the case who is verbally presenting the case. The live teaching case is different from a guest speaker event as it is more focused around specific decision points, such as a written case would be. We believe that the live teaching case approach alleviates several of the traditional case method shortcomings while maximizing the benefits associated with the presence of a guest speaker in class. This paper outlines the various steps involved in the live teaching case including initiating contact, planning the decision points, selecting student readings and developing pre-course materials, guiding the initial presentation and discussion, guiding the presentation and discussion of the managerial decision points, and class wrap-up. This approach is explained and then illustrated using three different IS courses, namely, an IS project management course, a systems analysis and design course, and a capstone course on enterprise system implementation. (Contains 1 figure, 2 tables, and 1 footnote.)

While organisations are increasingly interested in artificial intelligence (AI), many AI projects encounter significant issues or even fail. To gain a deeper understanding of the issues that arise during these projects and the practices that contribute to addressing them, we study the case of Consult, a North American AI consulting firm that helps organisations leverage the power of AI by providing custom solutions. The management of AI projects at Consult is a multi‐method approach that draws on elements from traditional project management, agile practices, and AI workflow practices. While the combination of these elements enables Consult to be effective in delivering AI projects to their customers, our analysis reveals that managing AI projects in this way draw upon three core logics, that is, commonly shared norms, values, and prescribed behaviours which influence actors' understanding of how work should be done. We identify that the simultaneous presence of these three logics—a traditional project management logic, an agile logic, and an AI workflow logic—gives rise to conflicts and issues in managing AI projects at Consult, and successfully managing these AI projects involves resolving conflicts that arise between them. From our case findings, we derive four strategies to help organisations better manage their AI projects. [ABSTRACT FROM AUTHOR]

Instant messaging (IM) and its use in organizations are investigated. Results suggest that critical mass represents an important factor for IM success in the workplace that IM symbolizes informality, and that IM is perceived to be much less rich than face-to-face communication.