Computer-Mediated Social Networks and Environmental Behavior

Research on environmental behavior highlights the role of information systems in the information acquisition and attitude formation process. Computer-mediated social networks (CMSNs) are forms of social structures that support social activity through computer-mediated communication and interactions. CMSNs can have an influential effect on social norms surrounding various issues, including environmental behavior. Further, CMSNs create a unique multi-nodal social environment within which individual behavior is virtually impacted, and eventually, physically modified. Extant research has not considered the social interactions that occur within a CMSN and how such interaction might further facilitate the adoption of environmental behaviors. This research aims to explore the influential role that CMSNs have on individuals through examination of specific attributes of a CMSN. More specifically, we examine CMSN Influencer, the message-giver within the CMSN, and CMSN Intensity, the user’s engagement in and usage of the CMSN

Cyber Citizens and Cyber Deviance: Exploring Social and Technical Factors as Antecedents to Cyber Deviance and the Implications for Cyber Citizenship

There is an alarming trend of individuals behaving inappropriately with information technology in both organizational and personal settings. For example, in a work context individuals are breaking repeatedly security protocols (Hovav et al. 2011) leading to the compromise of sensitive customer and important organizational data resources. Information systems (IS) researchers have studied the manners by which employees use, and misuse, information technology within organizations (Warkentin et al. 2009). Alternatively, in a non-work context, attention has been given to digital media piracy (Siponen et al. 2012) which continues to plague organizations. These examples illustrate some unacceptable behaviors exhibited by cyber citizens (Anderson et al. 2010) – both in the work and non-work settings. Typically, the boundaries of acceptable and unacceptable cyber behavior are defined by government authorities, who develop laws around specific illegal online behaviors, and businesses, who detail inappropriate actions in their terms of service agreements. Past research has shown that even with detailed guidelines, employees continue to cause breaches in security (Harris 2012) and these actions are greatly influenced by the behaviors of their coworkers (Gallivan et al. 2005). Alternatively, in a non-work environment, computer users operate within a precarious social and technological environment where the guidelines on appropriate behavior are vague (Nowak 2011). In such an ill-defined context, individuals tend to refer to accepted social norms as a guide, rather than try to understand the disparity between laws and policies (Morrison 1994). Research in criminology, sociology, and management, has focused on deviant behaviors with technology, termed cyber deviance. This term refers to inappropriate or criminal behavior in a digital context (Holt et al. 2010). Technical solutions are only marginally effective as deviant cyber behavior continues to proliferate (Rogers et al. 2006). Research has examined individual factors, such as self-control, in order to explain cyber deviant behaviors (Hinduja et al. 2008). Social factors have begun to be examined through the use of social learning theory and social cognitive theory to explain the ways in which attitudes and beliefs influence cyber deviance (Jacobs et al. 2012). Although studies that examine the individual, social, and technical factors have provided insight into cyber deviance behavior, they have not considered such behavior as part of cyber citizenship, where individuals behave in an ethical and productive manner in online environments. Additionally, recent IS research has begun to explore alternative ways to influence social norms and attitudes on individual behaviors in online environments in order to create more conscientious cyber citizens (Anderson et al. 2010). Therefore, this study aims to examine the individual, social, and technical factors that impact one’s intention to engage in cyber deviance. In this study, we focus on the illegal activities that occur in a digital environment. This highlights the unique actions that occur between humans and technology, suggesting this study is best served from such a perspective. Scialdone (2010) provides human computer interaction (HCI) researchers with a useful framework for examining phenomena that occur between humans and technology. Researchers must identify the human, the technology, the tasks, and the context to clearly situate their study in the HCI literature (Scialdone 2010). In this study we focus on average PC users and their interactions in computer-mediated social networks (CMSN). We examine individual factors, Perceived Utility of Cyber Deviance and Self-efficacy in Cyber Deviance; social factors, Cyber Citizen Social Norms on Cyber Deviance, and technical factors, CMSN Influencer and CMSN Intensity. The proposed research methodology is a quantitative approach using a quasi-experimental setup (Bhattacherjee 2012) through the use of scenarios which describe situations of cyber deviance. The use of scenarios allows us to provide a specific situation which serves as a reference point for our inquiry into the concepts of interest (Nagin and Paternoster 1993)

Objective Perfusion Assessment in Gracilis Muscle Interposition—A Novel Software-Based Approach to Indocyanine Green Derived Near-Infrared Fluorescence in Reconstructive Surgery

Background: Gracilis muscle interposition (GMI) is an established treatment option for complex perineal fistulas and reconstruction. The outcome is limited by complications such as necrosis, impaired wound healing and fistula persistence or recurrence. Quantifiable methods of assessing muscle flap perfusion intraoperatively are lacking. This study evaluates a novel and objective software-based assessment of indocyanine green near-infrared fluorescence (ICG-NIRF) in GMI. Methods: Intraoperative ICG-NIRF visualization data of five patients with inflammatory bowel disease (IBD) undergoing GMI for perineal fistula and reconstruction were analyzed retrospectively. A new software was utilized to generate perfusion curves for the specific regions of interest (ROIs) of each GMI by depicting the fluorescence intensity over time. Additionally, a pixel-to-pixel and perfusion zone analysis were performed. The findings were correlated with the clinical outcome. Results: Four patients underwent GMI without postoperative complications within 3 months. The novel perfusion indicators identified here (shape of the perfusion curve, maximum slope value, distribution and range) indicated adequate perfusion. In one patient, GMI failed. In this case, the perfusion indicators suggested impaired perfusion. Conclusions: We present a novel, software-based approach for ICG-NIRF perfusion assessment, identifying previously unknown objective indicators of muscle flap perfusion. Ready for intraoperative real-time use, this method has considerable potential to optimize GMI surgery in the future

The Predictive Value of Amplitude-Integrated Electroencephalography in Preterm Infants for IQ and Other Neuropsychological Outcomes at Early School Age

Background: Amplitude-integrated electroencephalography (aEEG) is used increasingly in neonatal intensive care and seems helpful in predicting outcomes at the age of 2 years, Objectives:To determine whether early aEEG patterns in preternn infants are equally useful in predicting outcomes at early school age. Methods: We recorded aEEG in 41 pre-terms (gestational age 26.0-32.9 weeks) at a median postnatal age of 9.7 h (IQR 7.0-25.3) and in 43 preternns on median day 8 (IQR 7-9). We assessed aEEG by pattern recognition and calculated the means of the aEEG amplitude centiles. At a median of 739 years, i.e., early school age, we assessed their motor, cognitive, and behavioral outcomes. Results: Depressed aEEG patterns were not associated with poorer outcomes. Cyclicity directly after birth was associated with a higher total IQ (mean 104 vs. 97, p = 0.05) and higher scores on visual perception (mean percentile 57.1 vs. 40.1, p 0,049) and visual memory (mean percentile 34.5 vs. 19.1, p = 0090). We found some associations between the aEEG amplitude centiles and cognitive outcomes, but none for motor or behavioral outcomes. There was an increased risk of abnormal scores on long-term verbal memory in cases of the lower 5th and 50th aEEG amplitude centiles directly after birth. The odds ratios were 0.65 (95% CI 0,42-0.99, p = 0.040) and 0.71 (95% CI 0.52-0.96, p = 0.025), respectively. Conclusions: In relatively healthy preternn infants the value of aEEG in predicting neuropsychological outcomes at early school age is limited. The presence of cyclicity directly after birth tends to be associated with better cognition. (C) 2018 The Author(s) Published by S. Karger AG, Base