Gamification risks in collaborative information systems: identification and management method.

In recent years, technology has been increasingly harnessed to play a role in encouraging and persuading people towards a better achievement of their individual and collective goals. Gamification solutions are popular approaches in this field. Gamification in business refers to the use of game elements in order to facilitate a change of behaviours, encourage engagement and increase motivation toward executing tasks and attaining goals. Despite the increasing recognition, previous research has revealed risks when applying gamification to teamwork within a business environment, such as negatively affect group coherence and creating adverse work ethics. For example, applying competitive elements such as leaderboards may lead to clustering amongst team members and encourage adverse work ethics such as intimidation and pressure. Although the problem is already recognised in principle, there is still a need to clarify and concretise those risks, their factors and their relation to the gamification dynamics and mechanics. Moreover, developing an integrated method to systematically identify those risks and provide a way to mitigate and prevent them for healthier and successful implementation of the system in teamwork places is needed. To achieve this goal, this thesis conducted a set of empirical studies involving managers, practitioners, psychologists and gamification users. This includes three-stage empirical research in two large-scale businesses using gamification in their workplace, including two months’ observation and interview study. This resulted in identifying a set of risk factors, a taxonomy of risks and set of management strategies. A follow-up focus groups research study also identified the modalities of application of these strategies, including who should be involved and how in their implementations. These studies first resulted in the development of a checklist tool to help identify gamification risks. The findings were finally used to develop a method to systematically identify gamification risks and recommend design practices and strategies to tackle them. By accomplishing that, this thesis recommends that gamification in enterprises shall undertake a risk assessment and management process to cater for its potential side effects on teamwork. A notable recommendation is to use participatory decision style for the method that enables for the analysis of gamification risks and their resolution. Moreover, this thesis recommends studying how to integrate the risk identification processes, which should take an iterative participatory style with the systems’ development life cycle activities

Digital Addiction: Negative Life Experiences and Potential for Technology-Assisted Solutions

There is a growing acceptance of the association between obsessive, compulsive and excessive usage of digital media, e.g., games and social networks, and users’ wellbeing, whether personal, economic or social. While specific causal relations between such Digital Addiction (DA) and the negative life experience can be debated, we argue in this paper that, nevertheless, technology can play a role in preventing or raising awareness of its pathological or problematic usage styles, e.g. through monitoring usage and enabling interactive awareness messages. We perform a literature review, with the primary aim of gathering the range negative life experiences associated with DA. We then conduct two focus groups to help gather users’ perception of the key findings from the literature. Finally, we perform a qualitative analysis of experts and practitioners’ interviews and comments from a user survey on DA warning labels. As a result, we develop eight families of the negative life experiences associated with DA, examine the role of software in facilitating the reduction of such negative experiences, and consider the challenges that may be encountered in the process

Performance test measurement.

Performance test measurement.</p

4-programs and 2-supercomputers example for the design model.

4-programs and 2-supercomputers example for the design model.</p

The <i>Gp</i> values variation when <i>Tp</i> changes for algorithm <i>BPC</i>.

The Gp values variation when Tp changes for algorithm BPC.</p

The <i>Gp</i> values for all algorithms when <i>n</i>_<i>su</i> according to the number of supercomputers.

The Gp values for all algorithms when nsu according to the number of supercomputers.</p

A secure solution based on load-balancing algorithms between regions in the cloud environment

The problem treated in this article is the storage of sensitive data in the cloud environment and how to choose regions and zones to minimize the number of transfer file events. Handling sensitive data in the global internet network many times can increase risks and minimize security levels. Our work consists of scheduling several files on the different regions based on the security and load balancing parameters in the cloud. Each file is characterized by its size. If data is misplaced from the start it will require a transfer from one region to another and sometimes from one area to another. The objective is to find a schedule that assigns these files to the appropriate region ensuring the load balancing executed in each region to guarantee the minimum number of migrations. This problem is NP-hard. A novel model regarding the regional security and load balancing of files in the cloud environment is proposed in this article. This model is based on the component called “Scheduler” which utilizes the proposed algorithms to solve the problem. This model is a secure solution to guarantee an efficient dispersion of the stored files to avoid the most storage in one region. Consequently, damage to this region does not cause a loss of big data. In addition, a novel method called the “Grouping method” is proposed. Several variants of the application of this method are utilized to propose novel algorithms for solving the studied problem. Initially, seven algorithms are proposed in this article. The experimental results show that there is no dominance between these algorithms. Therefore, three combinations of these seven algorithms generate three other algorithms with better results. Based on the dominance rule, only six algorithms are selected to discuss the performance of the proposed algorithms. Four classes of instances are generated to measure and test the performance of algorithms. In total, 1,360 instances are tested. Three metrics are used to assess the algorithms and make a comparison between them. The experimental results show that the best algorithm is the “Best-value of four algorithms” in 86.5% of cases with an average gap of 0.021 and an average running time of 0.0018 s