Mobility and Security in the New Way of Working: Employee Satisfaction in a Choose Your Own Device(CYOD) Environment

The consumerization of IT, known as Bring Your Own Device (BYOD), is an inevitable component in the future IT infrastructure of organizations. It is not the question if employees will use consumer IT products for their work, but how and under which conditions. The use of personalized mobile devices may be beneficial for both the employee and organization, but the concern of IT executives, on corporate data residing on uncontrolled mobile devices, is often leading to a restrictive policy. Giving employees the ability to choose from a variety of secure devices, at the expense of the organization, Choose Your Own Device (CYOD), may well bring the best of two worlds. In this research 126 employees at four multinational organizations were surveyed on their perception of usability and satisfaction of devices for their knowledge tasks. The outcomes were matched against a Risk Assessment on seven identified IT threats. The results show that a majority (52%) believes their performance would improve, when given the ability to choose a device of their own. The Risk Assessment shows that IT security risks do not need to increase, provided that the proper security policies are in place. This implies that the performance and satisfaction of employee can improve in a secure CYOD environment

Security-Based BYOD Risk Assessment Metamodelling Approach

Rapid changes in mobile computing and modern devices, for example, smartphones, tablets and iPads encouraged the employees to use their personal devices at the workplace. Bring Your Own Devices (BYOD) phenomenon has become pervasive and on-demand for business purposes. Nowadays, employees are allowed to bring personal devices to their workplace. Nevertheless, organizations are practicing BYOD to increase efficiency, work productivity, and cost-saving which lead to employee’s satisfaction. However, BYOD may cause harm in an organization if there are no security policies, regulations and management of the employee’s devices. The common security threats engaged to BYOD implementation are data leakage, exposed to malicious malware and sensitive corporates information. Hence, this study proposed a strategic solution, which is Security-Based BYOD Risk Assessment Metamodel (Security-Based BYODRAM) in reducing BYOD-related issues. The existing BYOD models were reviewed to identify the important concepts in the metamodel development. The Meta Object Facility (MOF) language was used to develop the proposed metamodel

Researching mobile learning: overview, September 2006 to September 2008

This is the summary of the report, which brought together the findings from the third phase of a two-year development and research project that focused on the impact of one-to-one personal ownership of mobile devices. Two areas emerged from the analysis as important in relation to impact, namely students' use of and attitudes to their mobile devices and the professional development of teachers

Mobile learning for delivering health professional education (protocol)

© 2015 The Cochrane Collaboration.This is the protocol for a review and there is no abstract. The objectives are as follows: The objective of this review is to evaluate the effectiveness of mLearning educational interventions for delivering pre-registration and post-registration healthcare professional education. We will primarily assess the impact of these interventions on students knowledge, skills, professional attitudes and satisfaction

The cross-contamination potential of mobile telephones

The use of mobile devices for professional, business, educational, personal and social purposes has accelerated exponentially over the last decade. Staff working in healthcare organisations, and patients and visitors using healthcare settings, understandably want to use mobile technology. Concerns have been raised about safety in terms of interference with equipment, and threats to privacy and dignity, yet less policy attention has been paid to infection risks. Healthcare professional students were supplied with smartphones as part of a larger educational project. Devices collected from a sub-sample of students working in operating theatre contexts were sampled to estimate the cross-contamination potential of the technology. A longitudinal multiple measures design was used. Under laboratory conditions, samples were taken from surfaces using swabbing techniques followed by contact plating. The devices were subsequently cleaned with 70% isopropyl alcohol and returned to the students. All devices demonstrated microbial contamination and over three quarters (86%) polymicrobial contamination. The technique and sites used to sample for microbial contamination influenced the levels of contamination identified. Swabbing alone was less likely to isolate polymicrobial contamination than contact plating, and some microorganisms were isolated only by contact plates and not by swabbing of the same area. The findings from this study demonstrate further research is urgently needed to inform evidence-based infection control policy on the use of personal equipment such as mobile devices in the healthcare settings where contamination may have adverse effects on patients, staff and visitors

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems

mHealth Research Applied to Regulated and Unregulated Behavioral Health Sciences

Behavioral scientists are developing new methods and frameworks that leverage mobile health technologies to optimize individual level behavior change. Pervasive sensors and mobile apps allow researchers to passively observe human behaviors “in the wild” 24/7 which supports delivery of personalized interventions in the real-world environment. This is all possible because these technologies contain an incredible array of sensors that allow applications to constantly record user location and can contextualize current environmental conditions through barometers, thermometers, and ambient light sensors and can also capture audio and video of the user and their surroundings through multiple integrated high-definition cameras and microphones. These tools are a game changer in behavioral health research and, not surprisingly, introduce new ethical, regulatory/legal and social implications described in this article