BACKGROUND:Voluntary turnover (VTO) of nursing employees is expensive for hospital systems and is often associated with lower levels of patient satisfaction, as well as adverse patient outcomes such as falls and medication errors.PURPOSE:The aim of this study was to establish nurses' electronic medical record (EMR) use patterns and test if they can be used to predict VTO.METHODOLOGY/APPROACH:The study followed 1,836 hospital nurses via the collection of EMR metadata through two 1-month time periods that were 1 year apart. Machine learning algorithms were then used to derive patterns of EMR utilization using VTO as a key variable for classification. Post hoc analysis of the most predictive variables was conducted.RESULTS:The predictive model was effective in identifying which nurses would turnover 73.4% of the time and which nurses would not turnover 84.1% of the time.PRACTICE APPLICATIONS:The ability to accurately predict nurses' intentions to leave is critical to reducing turnover. Early identification can lead to specific interventions to mitigate factors that are adversely impacting the nursing experience. Post hoc analysis and the key informant interviews indicated that many nurses do not appear to have good EMR navigation skills and spend significant effort in search of patient information.

The growth of social media has crossed the boundary from individual to organizational use, bringing with it a set of benefits and risks. To mitigate these risks and ensure the benefits of social media use are realized, organizations have developed a host of new policies, procedures, and hiring practices. However, research to date has yet to provide a comprehensive view on the nature of risk associated with the use of social media by organizations. Using a multi-panel Delphi approach consisting of new entrants to the workforce, certified human resource professionals, and certified Information Technology auditors, this study seeks to understand organizational social media risk. The results of the Delphi panels are compared against a textual analysis of 40 social media policies to provide a comprehensive view of the current state of social media policy development. We conclude with directions for future research that may guide researchers interested in exploring social media risk in organizations.

Detecting scareware messages that seek to deceive users with fear-inducing words and images is critical to protect users from sharing their identity information, money, and/or time with bad actors. Through a scenario-based experiment, the present study evaluated factors that aid users in perceiving deceptive communications. An online experiment was administered yielding 213 usable responses. The data from the study indicate high levels of deception detection self-efficacy and source trustworthiness increase the likelihood an individual will perceive a scareware message as deceptive. Additionally, technology awareness enhances self-efficacy to detect deception and reduces individual perceptions of source trustworthiness. Finally, the data significantly illustrate behavioral intention to use scareware is lower when the message is perceived as deceptive.

The use of mobile applications continues to experience exponential growth. Using mobile apps typically requires the disclosure of location data, which often accompanies requests for various other forms of private information. Existing research on information privacy has implied that consumers are willing to accept privacy risks for relatively negligible benefits, and the offerings of mobile apps based on location-based services (LBS) appear to be no different. However, until now, researchers have struggled to replicate realistic privacy risks within experimental methodologies designed to manipulate independent variables. Moreover, minimal research has successfully captured actual information disclosure over mobile devices based on realistic risk perceptions. The purpose of this study is to propose and test a more realistic experimental methodology designed to replicate real perceptions of privacy risk and capture the effects of actual information disclosure decisions. As with prior research, this study employs a theoretical lens based on privacy calculus. However, we draw more detailed and valid conclusions due to our use of improved methodological rigor. We report the results of a controlled experiment involving consumers (n=1025) in a range of ages, levels of education, and employment experience. Based on our methodology, we find that only a weak, albeit significant, relationship exists between information disclosure intentions and actual disclosure. In addition, this relationship is heavily moderated by the consumer practice of disclosing false data. We conclude by discussing the contributions of our methodology and the possibilities for extending it for additional mobile privacy research.

Mobile applications continue to experience explosive growth. Using mobile apps often requires disclosing location data—often along with various other forms of private information. Existing research has implied that consumers are willing to accept privacy risks for relatively smaller benefits and the mobile app context appears to be no different. In other words, consumers do not demonstrate perfect rationality regarding their valuation of risks and benefits regarding mobile app information disclosure. This study employs a theoretical lens based on privacy calculus, but integrated with prospect theory and intertemporal choice to explain how and why this "bounded" rationality occurs in information disclosure decisions through mobile apps. It reports the results of a controlled experiment involving consumers (n=1025) in a range of ages, education, and employment experience based on actual information disclosure. We find that consumers undervalue the probability of risks and have difficulty separating their existing risk exposure from potential new threats.

Mobile applications continue to experience explosive growth. Using mobile apps often requires disclosing location data—often along with various other forms of private information. Existing research has implied that consumers are willing to accept privacy risks for relatively smaller benefits and the mobile app context appears to be no different. In other words, consumers do not demonstrate perfect rationality regarding their valuation of risks and benefits regarding mobile app information disclosure. This study employs a theoretical lens based on privacy calculus, but integrated with prospect theory and intertemporal choice to explain how and why this "bounded" rationality occurs in information disclosure decisions through mobile apps. It reports the results of a controlled experiment involving consumers (n=1025) in a range of ages, education, and employment experience based on actual information disclosure. We find that consumers undervalue the probability of risks and have difficulty separating their existing risk exposure from potential new threats.

IT project governance is plagued by an inability to stop projects that ultimately fail, resulting in the loss of scarce resources and IT departments unable to generate full value for money invested. This study investigates the impact of the measurement information framework on information sufficiency, a key factor in reaching effective decisions. Specifically, this research addresses the question “Does a Balanced Scorecard (BSC) measurement information framework provide higher information sufficiency and greater decision making efficacy than the traditional Quality-Cost-Schedule approach in a project governance context?” This question is addressed using a randomized counterbalanced experimental design. Results were encouraging with significant support for two of the three hypotheses positing improved outcomes from use of the Balanced Scorecard framework.

IT project management is plagued by its inability to stop projects that ultimately fail. This persistent problem results in the loss of scarce resources and IT departments unable to generate full value for money invested. There is considerable evidence to suggest that information asymmetry is a significant contributor to this waste. The Balanced Scorecard is a framework that has been widely employed in business as a tool to translate the organizational vision into workable business plans and a framework for effective communication amongst stakeholders. Stage-gates are a widely-used method in new product development and they are gradually making inroads into software project governance. Using a model based on Balanced Scorecard and Stage-gates, this study proposes how their use in IT project governance can mitigate the effects of information asymmetry and thereby increase the likelihood of terminating an uneconomical project quickly.