Modeling Evacuation Risk Using a Stochastic Process Formulation of Mesoscopic Dynamic Network Loading

One of the actions usually conducted to limit exposure to a hazardous event is the evacuation of the area that is subject to the effects of the event itself. This involves modifications both to demand (a large number of users all want to move together) and to supply (the transport network may experience changes in capacity, unusable roads, etc.). In order to forecast the traffic evolution in a network during an evacuation, a natural choice is to adopt an approach based on Dynamic Traffic Assignment (DTA) models. However, such models typically give a deterministic prediction of future conditions, whereas evacuations are subject to considerable uncertainty. The aim of the present paper is to describe an evacuation approach for decision support during emergencies that directly predicts the time-evolution of the probability of evacuating users from an area, formulated within a discrete-time stochastic process modelling framework. The approach is applied to a small artificial case as well as a real-life network, where we estimate users' probabilities to reach a desired safe destination and analyze time dependent risk factors in an evacuation scenario

The Co-Creation of Value: Exploring User Engagement in User-Generated Content Websites

Organizational interest in user-generated content (UGC) websites is growing, as organizations face highly competitive markets, uncertain economic environments, and a growing user base accustomed to active engagement rather than passive acceptance of products and services. Organizations are now exploring ways to provide a platform (website) through which users generate and contribute content, resulting in a co-created experience between users and organizations. However, organizations interested in leveraging UGC websites are facing a new challenge – getting users to actively engage through content contribution, retrieval, and exploration. Thus, the research questions guiding this manuscript are: what factors influence an individual’s user experience in UGC websites and how does the user experience impact individual engagement behavior? This manuscript develops a theory of co-created value to examine how social interactions, operationalized as perceived dialogue, social accessibility, transparency, and risk, and technical features, operationalized as the perceived granularity, extensibility, integration, and evolvability, of a UGC website influence an individual’s user experience and subsequent engagement behaviors. A theoretical model is proposed and propositions are presented for the individual relationships. Implications and future directions for research are also discussed

An approach to define pedestrian escape routes in evacuation

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Applying Team-Based Learning in Online Introductory Information Systems Courses

Over the last two decades, the academy has experienced a renaissance of diversity in pedagogical techniques with the introduction of experiential learning, active learning, flipping the classroom, and, more recently, team-based learning (TBL). TBL adopts a two-stage process that incorporates individual learning with team collaboration. While frequently implemented in a face-to-face classroom, TBL has received limited attention in the online learning environment where geographically distributed, asynchronous learning poses challenges to its fundamental design. In particular, coordination costs and sequential inter-dependencies within the learning experience create unique challenges to online environments where students use limited communication channels compared to the traditional, face-to-face environments. This teaching tip discusses the authors’ experiences translating the principles of TBL and its learning sequence to an online introductory information systems course. We present instructor observations and qualitative feedback from students as the approach was implemented, including a model that outlines key activities in its implementation. We then conclude with a series of teaching suggestions to fellow academics seeking to adapt TBL to the online environment in their courses

Two Variables Algorithms for Solving the Stochastic Equilibrium Assignment with Variable Demand: Performance Analysis and Effects of Path Choice Models

In this paper a general fixed-point approach dealing with multi-user (stochastic) equilibrium assignment with variable demand is proposed. The main focus is on (i) the implementation and comparison of different algorithm solutions based on successive averages methods calculated on one (arc flows, arc costs) and on two variables (arc flows and path satisfaction; arc costs and demand flows); (ii) the effects of algorithm efficiency on different path choice models and/or travel demand choice models. In terms of the best performing algorithmic solution, the effects of different path choice models, such as Multinomial Logit model, C-Logit model and Multinomial Probit model were implemented, and algorithmic efficiency was investigated w.r.t. a real network

User innovations through online communities from the perspective of social network analysis

Organizations have begun to leverage both internal and external sources for innovation. Specifically, organizations are increasingly relying on end users that engage via user innovation communities to identify potentially valuable ideas for an organization to adopt. However, research has shown that organizational success in leveraging these communities relies on a thorough understanding of how users behave within the community. The purpose of this manuscript is to provide further analysis and develop a richer understanding of user behavior in the Dell IdeaStorm user innovation community. Findings illustrate different patterns of user behaviors when they comments or rate posted idea

Providing Theoretical Foundations: Developing an Integrated Set of Guidelines for Theory Adaptation

Developing and advancing theory in the information systems (IS) discipline requires scholars to use and contribute to theory. While few IS scholars create new theories, many borrow and adapt theories from other disciplines to study a variety of phenomena in the realm of IS. Over time, this practice has raised concerns as to the appropriateness and quality of theories adapted in the discipline. In particular, this practice causes issues when one considers conflicting results from many studies that claim to leverage the same theoretical foundation. We examine the issues surrounding theory adaptation in IS and provide a set of integrated theory adaptation guidelines to help scholars successfully and reliably adapt theory. We illustrate how one might use our guidelines via using Protection Motivation Theory in an organizational information security setting

Risk perceptions about personal Internet-of-Things: Research directions from a multi-panel Delphi study

Internet-of-Things (IoT) research has primarily focused on identifying IoT devices\u27 organizational risks with little attention to consumer perceptions about IoT device risks. The purpose of this study is to understand consumer risk perceptions for personal IoT devices and translate these perceptions into guidance for future research directions. We conduct a sequential, mixed-methods study using multi-panel Delphi and thematic analysis techniques to understand consumer risk perceptions. The results identify four themes focused on data exposure and user experiences within IoT devices. Our thematic analysis also identified several emerging risks associated with the evolution of IoT device functionality and its potential positioning as a resource for malicious actors to conduct security attacks

Could molecular assessment of calcium metabolism be a useful tool to early screen patients at risk for pre-eclampsia complicated pregnancy? Proposal and rationale.

Abstract One of the most frequent causes of maternal and perinatal morbidity is represented by hypertensive disorders during pregnancy. Women at high risk must be subjected to a more intensive antenatal surveillance and prophylactic treatments. Many genetic risk factors, clinical features and biomarkers have been proposed but none of these seems able to prevent pre-eclampsia onset. English literature review of manuscripts focused on calcium intake and hypertensive disorders during pregnancy was performed. We performed a critical analysis of evidences about maternal calcium metabolism pattern in pregnancy analyzing all possible bias affecting studies. Calcium supplementation seems to give beneficial effects on women with low calcium intake. Some evidence reported that calcium supplementation may drastically reduce the percentage of pre-eclampsia onset consequently improving the neonatal outcome. Starting from this evidence, it is intuitive that investigations on maternal calcium metabolism pattern in first trimester of pregnancy could represent a low cost, large scale tool to screen pregnant women and to identify those at increased risk of pre-eclampsia onset. We propose a biochemical screening of maternal calcium metabolism pattern in first trimester of pregnancy to discriminate patients who potentially may benefit from calcium supplementation. In a second step we propose to randomly allocate the sub-cohort of patients with calcium metabolism disorders in a treatment group (calcium supplementation) or in a control group (placebo) to define if calcium supplementation may represent a dietary mean to reduce pre-eclampsia onset and to improve pregnancy outcome