Cosmic Ray Feedback on Bi-stable ISM Turbulence

Despite being energetically important, the effect of cosmic rays on the dynamics of the interstellar medium (ISM) is assumed to be negligible because the cosmic ray energy diffusion coefficient parallel to the magnetic field is relatively large. Using numerical simulations, we explore how variation of the cosmic ray diffusion coefficient as a function of gas temperature could impact the dynamics of the ISM. We create a two-zone model of cosmic ray transport, reflecting the strong damping of the small scale magnetic field fluctuations, which scatter the cosmic rays, in a gas with low ionization. The variable diffusion coefficient allows more cold gas to form. However, setting the diffusion coefficient at a critical value in the warm phase allows the cosmic rays to adjust the kinetic energy cascade. Specifically, we show the slope of the cascade changes for motion perpendicular to the mean magnetic field, whereas kinetic energy parallel to the magnetic field is reduced equally across inertial scales. We show that cosmic ray energization (or reacceleration) comes at the expense of total radiated energy generated during the formation of a cold cloud. We also show that our two-zone model of cosmic ray transport is capable of matching estimates of the grammage for some paths through the simulation, but full comparison of the grammage requires simulating turbulence in a larger volume.Comment: Submitted to ApJ. 19 pages, 11 figures, 4 tables. Comments welcom

Understanding the Whistle-blowing Intention to Report Breach of Confidentiality

We examine the factors that encourage employees to whistle-blow wrongdoings in relation to confidentiality breaches. We investigate how their anticipated regret about remaining silent changes over time, how such changes influence their whistle-blowing intentions, and what employee characteristics and organizational policies moderate this relationship. Drawing on attribution theory, we develop three hypotheses. Our experiment findings show that: 1) employees’ perceptions of the controllability and intentionality (but not stability) of the wrongdoing act affect how their anticipated regret evolves, 2) anticipated regret increases employees’ whistle-blowing intentions, 3) anticipated regret has a stronger effect on whistle-blowing intentions when organizations implement policies that promote efforts to protect information confidentiality, and 4) employees with information technology knowledge have a stronger intention to whistle-blow. Theoretically, our study extends the organization security literature’s focus to individuals’ whistle-blowing and highlights an IS research agenda around whistle-blowing in relation to confidentiality breaches. Practically, it informs organizations about how to encourage employees to whistle-blow when they observe confidentiality breaches

Does Chatting Really Help? Tweet Analytics and Analyst Forecast Dispersion

Financial analysts use tweet analytics to prepare their forecasts, yet little information that describes how they do so exists. To address this gap, we scrutinize the associative relationships between tweets about a company’s service and the dispersion of analyst forecasts about the same company’s financial performance. We developed three sets of hypotheses. We extracted tweets related to airlines from the Twitter data from Archive Team and analyst forecast data from Institutional Brokers’ Estimate System Academic. We obtained airline-related tweets from nearly 200,000 individual Twitter users about 10 airlines during a 55-month study period and ran multiple regressions to test the associations between tweet characteristics and forecast dispersion. Our results suggest that, when more posters generate more tweets about a company’s service, analysts make less dispersed forecasts. In addition, negative (or non-verified) tweets reduce forecast dispersion to a greater extent than positive (or verified) tweets do. Theoretically, this paper confirms that Twitter can be a useful data source to provide analysts with additional information to prepare their forecasts. Practically, our findings provide empirical evidence about how Twitter data is associated with analyst forecast dispersion. We encourage stakeholders (such as analysts from small firms and individual investors) to extract data from Twitter as a supplement to market information when analyzing data