Abstract: We conducted two surveys of Americans who reported that they drive at least 3 times per week and own a smartphone. We asked them about distracted driving behaviors, risk perceptions of distracted driving, attitudes towards driving and their cell phones, and their attitudes towards methods of reducing distracted driving behavior.
Authors: Brittany Shoots-Reinhard, Ellen Peters
Date: November 2018
Abstract: Distracted driving is a leading contributor to motor vehicle accidents, and it is estimated to cause thousands of deaths and hundreds of thousands of injuries per year in the United States. We conducted two surveys of U.S. drivers to study the psychological underpinnings of distracted driving. We considered a number of possible causes including 1) underestimation of distracted driving risks, 2) affective reactions to causes and consequences of distracted driving, 3) motivated denial of risks of distracted driving, 4) overconfidence in driving ability, and 5) perceived acceptability of distracted driving. We also examined support for a variety of methods of distraction reduction and began investigating ways to increase support for distraction mitigation. We found evidence of multiple independent predictors toward self-reported distracted driving, variability in support for distraction mitigation, and confirmed that the language used to describe mitigation strategies influences support.
Presentation at the Community Engagement Conference. The Ohio State University, Columbus, Ohio, January 23–24, 2019.
Authors: Brittany Shoots-Reinhard, Ellen Peters
Date: January 23, 2019
Abstract: This study evaluates the influences of built environment on the frequency and severity of vehicle crashes with focuses on a comparative analysis between the crashes caused by distracted driving and non-distracted driving. Using a comprehensive dataset with 1.4 million crash records in Ohio for the period 2013 – 2017 as an example, the relationships between built environments and the frequency and severity of vehicle crashes caused by distracted driving were examined using negative binomial regression and generalized order logit regression methods. Our study reveals that built environments, such as the length of a roadway segment, number of lanes, the location of the road (being in an urban area) have positive associations with crash frequencies. Conversely, other road features, such as median and a shoulder with asphalt pavement were found to have negative associations with crash frequencies caused by distracted driving. The outcomes of severity analysis confirm that distracted driving related crashes tend to be more severe than non-distracted driving related crashes in certain road environments. In particular, vehicle crashes caused by distracted driving were found to be more severe if the accident occurs at work zones or on interstate highways. On the other hand, roundabout was confirmed to be effective in reducing crash severities in general, but with a more significant effect on mitigating the severity of DD related crashes.
Authors: Zhenhua Chen, Youngbin Lym
Date: Summer 2018
Abstract: Each year in the United States, weather-related power outages result in billions of dollars of restoration costs and economic losses. Utility companies, emergency management agencies, and other public and private entities affected by power outages attempt to anticipate and mitigate the effect of these outages by utilizing power outage prediction models. These models are typically developed for either a combination of weather events or specialized for specific weather events like tropical cyclones. Despite the fact that thunderstorms account for almost half of major power outage events, development of specialized models for thunderstorms is at an early stage. This study uses the random forest machine learning technique to develop specialized models for thunderstorm related power outage events. The models are trained using power outage data from 31 thunderstorm events along with 75 predictor variables that include forecast weather conditions and environmental variables that have been found to improve power outage prediction models in past research. Results showed modest model skill compared to baseline models. Variable importance measures showed that environmental variables had high importance and convective hazard probabilities issued by NOAA’s National Weather Service Storm Prediction Center (SPC) had low importance. This low importance of convective hazard probabilities potentially decreased model skill and we hypothesize that it is related to the spatial scale used in this study. Additionally, it is noted that the model has a tendency to underpredict outages in more intense thunderstorm events.
Authors: Stephen A. Shield, Steven M. Quiring, D. Brent McRoberts
Date: June 22, 2018
Abstract: A major problem in the field of management has been the wide variety of definitions that have been proposed. To resolve the resulting terminological confusion, a conceptual review that considers the many ways in which risk has been defined in the management literature was conducted. Our findings highlight some disagreement in the management literature associated with the definition of risk. The review identified four distinct groups of risk definitions: (1) risk as outcome uncertainty, (2) risk as probability of (unwanted) outcomes, (3) risk as variability in outcomes, and (4) risk as unwanted outcomes (that may or may not occur).
Authors: Jonas Stromfeldt Eduardsen, Svetla Trifonova Marinova, Oded Shenkar, Simcha Ronen
Date: March 2, 2018
Abstract: This paper shares initial observations and quotes derived from semi-structured interviews with corporate data ethics practitioners that took place from 2018-2019. Quotes provide first-hand accounts of the privacy, fairness and other ethical issues encountered when managing big data analytics. Emerging processes are noted, in addition to substantive frameworks that firms are employing to address recognized issues. Hypotheses are then made as to why the Corporate Data Ethics field is developing and why different companies may approach the task of data ethics management differently. Future areas of research, including an ongoing survey effort, are noted.
Authors: Dennis D. Hirsch, Tim Bartley, Aravind Chandrasekaran, Srinivasan Parthasarathy, Piers Norris Turner, Davon Norris, Keir Lamont, Christina Drummond
Date: September 10, 2019
Abstract: We document that localized policies designed to mitigate climate risk can lead to regulatory arbitrage by firms, resulting in unintended consequences. Using detailed plant-level data, we investigate the impact of the most extensive regional climate policy in the United States, the California cap-and-trade program, on corporate real activities such as greenhouse gas emissions and plant ownership. We show that industrial plants governed by the policy reduce emissions in California when the parent company is financially constrained, but that these firms internally reallocate their emissions to plants located in other states. Similarly, constrained firms are more likely to reduce ownership in Californian plants and increase ownership in plants outside California. In contrast, unconstrained firms generally do not ad-just plant emissions and ownership either in California or in other states. Overall, firms do not reduce their total emissions when part of their assets are affected by the regulation, but in fact, increase them if financially constrained. The results document real spillover effects stemming from resource reallocations by constrained firms to avoid regulatory costs, undermining the effectiveness of localized policies. Our study has important implications for the current debate on global climate policy agreements.
Authors: Söhnke M. Bartram, Kewei Hou, Sehoon Kim
Date: October 7, 2018; Last revised: October 22, 2019