This study proposes that leaders and followers in university team projects should have authentic leadership (AL) themselves in order to improve the overall performance of the team project. While previous studies have focused mainly on achieving performance through AL on the firm level, this study endeavored to examine the relationship between university students who are expected to serve in an organization’s human resources department in the future and followers’ project performance (FPP). This study also considers both followers’ positive psychological capital (FPPC) and their authentic followership (AF), which can be affected by the degree of AL. In order to verify the hypotheses, we adopted a partial least square-structural equation model (PLS-SEM) with 175 samples of valid data from two universities in South Korea. Results showed that all four hypotheses, including the direct and indirect effects, were significantly corroborated. In compliance with these results, this study suggests that a leader’s AL should be a prerequisite to improve FPP. Furthermore, this study establishes the importance of FPPC through the fact that when a leader is authentic, FPPC and FPP can be cultivated. Having compared our findings with previous research, we predict that if students develop AL at university before becoming part of an organization, they will contribute to the performance of the university as well as to attaining the organization’s sustainable performance as a member.

Background De-identifying personal information is critical when using personal health data for secondary research. The Observational Medical Outcomes Partnership Common Data Model (CDM), defined by the nonprofit organization Observational Health Data Sciences and Informatics, has been gaining attention for its use in the analysis of patient-level clinical data obtained from various medical institutions. When analyzing such data in a public environment such as a cloud-computing system, an appropriate de-identification strategy is required to protect patient privacy. Objective This study proposes and evaluates a de-identification strategy that is comprised of several rules along with privacy models such as k-anonymity, l-diversity, and t-closeness. The proposed strategy was evaluated using the actual CDM database. Methods The CDM database used in this study was constructed by the Anam Hospital of Korea University. Analysis and evaluation were performed using the ARX anonymizing framework in combination with the k-anonymity, l-diversity, and t-closeness privacy models. Results The CDM database, which was constructed according to the rules established by Observational Health Data Sciences and Informatics, exhibited a low risk of re-identification: The highest re-identifiable record rate (11.3%) in the dataset was exhibited by the DRUG_EXPOSURE table, with a re-identification success rate of 0.03%. However, because all tables include at least one “highest risk” value of 100%, suitable anonymizing techniques are required; moreover, the CDM database preserves the “source values” (raw data), a combination of which could increase the risk of re-identification. Therefore, this study proposes an enhanced strategy to de-identify the source values to significantly reduce not only the highest risk in the k-anonymity, l-diversity, and t-closeness privacy models but also the overall possibility of re-identification. Conclusions Our proposed de-identification strategy effectively enhanced the privacy of the CDM database, thereby encouraging clinical research involving multiple centers.

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The purpose of this study was to evaluate from a consumer's perspective the content of digital video advertisements posted on the website YouTube.com. Advertisements for the top-selling 25 prescription drugs were identified during a 1-month time period (September 2009) and were categorized as either regulated or unregulated by the FDA. Unregulated advertisements were further classified as educational (by health care professionals), commercial (to sell drugs or for lawsuit claims), or miscellaneous. Drug name, advertisement type, number of views, and rating by viewers were collected. The current sample consists of 11.4% FDA-regulated and 88.6% FDA-unregulated (15.4% educational, 5.8% commercial, and 88.8% miscellaneous) advertisements. FDA-unregulated advertisement videos had a higher number of average views (6318 + 10,325) compared with FDAregulated videos (6044 + 10,991). As consumers are exposed to a large number of FDA-unregulated advertisements for prescription drugs on YouTube, education is needed to enable consumers to distinguish between FDA-regulated and FDA-unregulated videos.

The visual fidelity of a virtual environment lacks the exceedingly complex layers from the physical world, but the continuous improvements of image rendering technology and computation powers have led to greater demands for virtual simulations. Our study employs Crime Prevention through Environmental Design (CPTED) as a risk control measure and utilizes two principles: Access Control and Natural Surveillance. We conducted an experiment with (n-sample: 100) graduate students. For the experiment, we utilized the Factor Analysis of Information Risk (FAIR) to quantitatively analyze the risk. Furthermore, we adopted the lme4 package for R to estimate the mixed effect of the 6,242,880 observations retrieved from Kaggle. Based on the two experiments, we were able to critically evaluate the contributions of CPTED through a multi-component analysis. Our study investigates how spatial syntax and territorial demarcation may translate in the cyberspace realm. We found that the corollaries of the mophology in the virtual environment effects the distribution of crime. The results of our study discusses how to determine the criminogenic designs and capacity in the cyberspace realm.