11 research outputs found
A Serious Game Design: Nudging Users’ Memorability of Security Questions
Online review communities thrive on contributions from different reviewers, who exhibit a varying range of community behaviors. However, no attempt has been made in the IS literature to cluster behavioral patterns across a reviewer population. In this paper, we segment the reviewers of a popular review site (Yelp) using two-step cluster analysis based on four key attributes (reviewer involvement, sociability, experience, and review quality), resulting in three distinct reviewer segments - Enthusiasts, Adepts, and Amateurs. We also compare the propensity of receiving community recognition across these segments. We find that the Enthusiasts, who show high involvement and sociability, are the most recognized. Surprisingly, the Adepts, who are high on review quality, are the least recognized. The study is a novel attempt on reviewer segmentation and provides valuable insights to the community managers to customize strategies to increase productivity of different segments
A Model for Enhancing Human Behaviour with Security Questions: A Theoretical Perspective
In recent years, technological improvements have provided a variety of new opportunities for insurance companies to adopt telematics devices in line with usage-based insurance models. This paper sheds new light on the application of big data analytics for car insurance companies that may help to estimate the risks associated with individual policyholders based on complex driving patterns. We propose a conceptual framework that describes the structural design of a risk predictor model for insurance customers and combines the value of telematics data with deep learning algorithms. The model’s components consist of data transformation, criteria mining, risk modelling, driving style detection, and risk prediction. The expected outcome is our methodology that generates more accurate results than other methods in this area
A Model for Enhancing Human Behaviour with Security Questions: A Theoretical Perspective
Security questions are one of the mechanisms used to recover passwords.
Strong answers to security questions (i.e. high entropy) are hard for attackers
to guess or obtain using social engineering techniques (e.g. monitoring of
social networking profiles), but at the same time are difficult to remember.
Instead, weak answers to security questions (i.e. low entropy) are easy to
remember, which makes them more vulnerable to cyber-attacks. Convenience leads
users to use the same answers to security questions on multiple accounts, which
exposes these accounts to numerous cyber-threats. Hence, current security
questions implementations rarely achieve the required security and memorability
requirements. This research study is the first step in the development of a
model which investigates the determinants that influence users' behavioural
intentions through motivation to select strong and memorable answers to
security questions. This research also provides design recommendations for
novel security questions mechanisms.Comment: 11, Australasian Conference on Information Systems, 201
Personal technology use amongst stroke patients : understanding the best platforms for the design of health interventions in treatment and rehabilitation
Europe's healthcare systems are under strain with an ageing population contributing to increased risk of strokes. Rapid technology adaption is needed to prevent, rehabilitate and manage symptoms. This paper identifies what technology platforms are most familiar and accessible to stroke patients to guide designers and engineers to develop future interventions. A survey was distributed to 100 inpatients at a stroke unit, identifying patients' accessibility and usage of personal technologies. Results showed that desktop/laptops and smartphones were most used as opposed to tablets and smartwatches. Different technologies were used for different tasks with a notable lack of devices used for personal health. The underlying reasons for this are discussed with recommendations made on what personal technology platforms should be implemented by designers and engineers in technology-based health interventions