Leveraging lessons learned in organizations through implementing practice-based organizational learning and performance improvement - An opportunity for context-based intelligent assistant support (CIAS)

Organizations that leverage lessons learned from their experience in the practice of complex real-world activities are faced with five difficult problems. First, how to represent the learning situation in a recognizable way. Second, how to represent what was actually done in terms of repeatable actions. Third, how to assess performance taking account of the particular circumstances. Fourth, how to abstract lessons learned that are re-usable on future occasions. Fifth, how to determine whether to pursue practice maturity or strategic relevance of activities. Here, organizational learning and performance improvement are investigated in a field study using the Context-based Intelligent Assistant Support (CIAS) approach. A new conceptual framework for practice-based organizational learning and performance improvement is presented that supports researchers and practitioners address the problems evoked and contributes to a practice-based approach to activity management. The novelty of the research lies in the simultaneous study of the different levels involved in the activity. Route selection in light rail infrastructure projects involves practices at both the strategic and operational levels; it is part managerial/political and part engineering. Aspectual comparison of practices represented in Contextual Graphs constitutes a new approach to the selection of Key Performance Indicators (KPIs). This approach is free from causality assumptions and forms the basis of a new approach to practice-based organizational learning and performance improvement. The evolution of practices in contextual graphs is shown to be an objective and measurable expression of organizational learning. This diachronic representation is interpreted using a practice-based organizational learning novelty typology. This dissertation shows how lessons learned when effectively leveraged by an organization lead to practice maturity. The practice maturity level of an activity in combination with an assessment of an activity’s strategic relevance can be used by management to prioritize improvement effort.

The right to humanitarian assistance in natural and human-made disasters: progress and challenges for an emerging international legal framework

Natural and human-made disasters cause on average 120,000 deaths and over US$140 billion in damage to property and infrastructure every year, with national, regional and international actors consistently responding to the humanitarian imperative to alleviate suffering wherever it may be found. Despite various attempts to codify international disaster laws since the 1920s, a right to humanitarian assistance remains contested, reflecting concerns regarding the relative importance of state sovereignty vis-à-vis individual rights under international law. However, the evolving acquis humanitaire of binding and non-binding normative standards for responses to humanitarian crises highlights the increasing focus on rights and responsibilities applicable in disasters; although the International Law Commission has also noted the difficulty of identifying lex lata and lex ferenda regarding the protection of persons in the event of disasters due to the “amorphous state of the law relating to international disaster response.” Therefore, using the conceptual framework of transnational legal process, this thesis analyses the evolving normative frameworks and standards for rights-holders and duty-bearers in disasters. Determining the process whereby rights are created and evolve, and their potential internalisation into domestic law and policy, provides a powerful analytical framework for examining the progress and challenges of developing accountable responses to major disasters.

Integrating mobile and cloud resources management using the cloud personal assistant

The mobile cloud computing model promises to address the resource limitations of mobile devices, but effectively implementing this model is difficult. Previous work on mobile cloud computing has required the user to have a continuous, high-quality connection to the cloud infrastructure. This is undesirable and possibly infeasible, as the energy required on the mobile device to maintain a connection, and transfer sizeable amounts of data is large; the bandwidth tends to be quite variable, and low on cellular networks. The cloud deployment itself needs to efficiently allocate scalable resources to the user as well. In this paper, we formulate the best practices for efficiently managing the resources required for the mobile cloud model, namely energy, bandwidth and cloud computing resources. These practices can be realised with our mobile cloud middleware project, featuring the Cloud Personal Assistant (CPA). We compare this with the other approaches in the area, to highlight the importance of minimising the usage of these resources, and therefore ensure successful adoption of the model by end users. Based on results from experiments performed with mobile devices, we develop a no-overhead decision model for task and data offloading to the CPA of a user, which provides efficient management of mobile cloud resources.