Research on information systems has often focused on various post-adoption behaviour, examining how users adapt to using IT artefacts. This study explores such post-adoption practices specifically for discontinuing mobile applications (apps) use. Instead of investigating a particular user’s behaviour, we focus on participants’ lived experiences with discontinuing app use. We employed an inductive grounded theory research approach, collecting data through semi-structured interviews with 38 participants. We identified two key types of post-adoption behaviour, engagement practices comprising workaround and reframing and disengagement practices comprising discontinuance and deferring. Using Expectation Disconfirmation Theory (EDT), we demonstrated how negative disconfirmation can be managed through post-adoption behaviour. Our main theoretical contribution extends EDT to include a new category of disengagement practices called “deferring.” Our findings also provide valuable practitioner insights for app retention strategies.

Aligning student skills with industry practices in the current fast-changing digital economy is one of the many challenges faced by higher education institutions. In this paper, we present a novel approach in which we developed a digital commons to serve as a bridge between emerging industry practices and academic curricula. In a digital commons, knowledge is created and shared online. This case study describes the motivation for, and the design, development and evaluation of, an industry practice digital commons (IPDC) to assist students in developing industry relevant knowledge. The target students were pursuing a course on IT Governance and Service Management (IGSM) at an Australian university. The development of the digital artifact followed the design science research (DSR) paradigm. Feedback from 78 students and 26 practitioners, and the visitor analytics totaling 122,360 users, indicated that both student and practitioner communities considered the IPDC to be valuable.

Cloud computing is gaining increasing interest as an enabling technology for improved productivity, efficiency and cost reduction. It also has the potential to add value to reliability and scalability of organisation’s systems, which would allow organisations to better focus on their core business and strategies. This research paper aimed at exploring the perceived potential for value creation from cloud computing in Australian Regional Governments. Using the Technology-Organization-Environment (TOE) framework, the Diffusion of Innovation (DOI) theory and Desires Framework (DF), a conceptual research model is devised. The key identified factors that are likely to influence adoption of cloud computing are identified along with the implications to current policies, anticipated benefits and challenges of adoption of cloud computing. The research employed a qualitative approach involving 21 in-depth interviews with Australian local councils’ IT managers. The findings of this research are mapped to the research model factors, thereby presenting and validating a number of value-adding concepts that are expected to contribute to investment decisions on cloud computing adoption.

Distance field from a surface geometry is used in several scientific algorithms and applications from computer graphics, visualization, computational fluid dynamics and more. Distance field can be calculated efficiently by solving the eikonal equation at each grid point using the fast-sweeping method. There has been an increased interest to develop parallel algorithms for the fast-sweeping method to accelerate its computational turnaround time. Most parallel strategies have focused on shared-memory parallelism and do not readily extend to distributed-memory parallelism to handle large-scale problems. To address this issue, we propose a domain-decomposition strategy to enable distributed-memory parallelism for the fast-sweeping method on heterogeneous computing clusters with accelerators. In our strategy, we make use of the Cuthill-McKee ordering for both fine and coarse-grain parallelism such that parallel computations proceed in direction of solution characteristics. We consider both CUDA and OpenACC implementations to compute the distance field from arbitrarily complex geometries and demonstrate parallel computations of large-scale problems that necessitate distributed-memory parallelism. We also discuss the implications of the proposed strategy for scalability of the fastsweeping method.