Why People Keep Using Knowledge Management Systems: A Causal Analysis of Continuance Behavior

This study examines the factors that cause a person to become a continuous user of a knowledge management system by examining continuance behavior. Continuance behavior is the decision to continue using a product after initial use. The data for this study were obtained using an online survey. The results were analyzed using partial least squares structural equation modeling. Six main hypotheses were developed which resulted in the evaluation of fourteen hypotheses. The results show that the technological features of a knowledge management system positively influence a user’s evaluation with limited influence from the system’s community features. The results produced a 58% coefficient of determination for knowledge management systems continuance intention and 37% for knowledge management systems continuance behavior. This investigation serves as a foundation for further research on the continuance usage of knowledge management systems. It addresses the needs of practitioners by examining which conditions they can manage to increase the purposeful use of their organizations’ knowledge management systems. The study also addresses the needs of academia by expanding the literature on continuance behavior of knowledge management systems.

Continuous Mobile User Authentication Using Combined Biometric Traits

Current state of the art authentication systems for mobile devices primarily rely on single point of entry authentication which imposes several flaws. For example, an attacker obtaining an unlocked device can potentially use and exploit it until the screen gets locked again. With continuous mobile user authentication, a system is embedded into the mobile devices, which continuously monitors biometric features of the person using the device, to validate if those monitored inputs match and therefore were made by the previously authenticated user. We start by giving an introduction towards the state of the art of currently used authentication systems and address related problems. For our main contribution we then propose, implement and discuss a continuous user authentication system for the Android ecosystem, which continuously monitors and records touch, accelerometer and timestamp data, and run experiments to gather data from multiple subjects. After feature extraction and normalization, a Hidden Markov Model is employed using an unsupervised learning approach as classifier and integrated into the Android application for further system evaluation and experimentation. The final model achieves an Area Under Curve of up to 100% while maintaining an Equal Error Rate of 1.34%. This is done by combining position and accelerometer data using gestures with at least 50 data points and averaging the prediction result of 25 consecutive gestures.

Authors’ Response to Peer Reviews of “Continuous User Experience Monitoring of a Patient-Completed Preoperative Assessment System in the United Kingdom: Cross-sectional Study” (Preprint)

UNSTRUCTURED These are author responses to peer review of ms#31679.

Peer Review of “Continuous User Experience Monitoring of a Patient-Completed Preoperative Assessment System in the United Kingdom: Cross-sectional Study” (Preprint)

UNSTRUCTURED This is a peer review report for ms#31679.

Peer Review of “Continuous User Experience Monitoring of a Patient-Completed Preoperative Assessment System in the United Kingdom: Cross-sectional Study” (Preprint)

UNSTRUCTURED This is a peer review report for ms#31679.

A Lightweight Privacy-Aware Continuous Authentication Protocol-PACA

As many vulnerabilities of one-time authentication systems have already been uncovered, there is a growing need and trend to adopt continuous authentication systems. Biometrics provides an excellent means for periodic verification of the authenticated users without breaking the continuity of a session. Nevertheless, as attacks to computing systems increase, biometric systems demand more user information in their operations, yielding privacy issues for users in biometric-based continuous authentication systems. However, the current state-of-the-art privacy technologies are not viable or costly for the continuous authentication systems, which require periodic real-time verification. In this article, we introduce a novel, lightweight, <underline>p</underline>rivacy-<underline>a</underline>ware, and secure <underline>c</underline>ontinuous <underline>a</underline>uthentication protocol called PACA. PACA is initiated through a password-based key exchange (PAKE) mechanism, and it continuously authenticates users based on their biometrics in a privacy-aware manner. Then, we design an actual continuous user authentication system under the proposed protocol. In this concrete system, we utilize a privacy-aware template matching technique and a wearable-assisted keystroke dynamics-based continuous authentication method. This provides privacy guarantees without relying on any trusted third party while allowing the comparison of noisy user inputs (due to biometric data) and yielding an efficient and lightweight protocol. Finally, we implement our system on an Apple smartwatch and perform experiments with real user data to evaluate the accuracy and resource consumption of our concrete system.