An actor-network theory study of public sector inter-organisational collaboration

EMBARGOED The literature on inter-organisational collaboration, although wide-ranging, offers little guidance on collaboration as process. It focuses in the main on human attributes like leadership, trust and agency, but gives little consideration to the role of objects in the development of inter-organisational collaborations. A central aim of this thesis is to understand the interaction of objects and humans in the development of a particular health and social care partnership in the North East of England. This socio-material perspective was achieved through actor-network theory (ANT) as a methodology, in which the researcher is equally sensitised to the role of human and non-human entities in the development of a network. The case study is that of the North East Lincolnshire Care Trust Plus (CTP). This was a unique health and social care collaboration arrangement between North East Lincolnshire Council and North East Lincolnshire Primary Care Trust, setup to address heath inequalities in the region. The CTP was conceived and developed at a local level by the respective organisation’s decision makers in the face of considerable opposition from regional policy makers and national regulators. However, despite this opposition, the directors eventually achieved their goal and the CTP became operational on 1st September 2007. This study seeks to understand how the CTP was conceived and developed, in the face of this opposition. The thesis makes a number of original contributions. Firstly, it adds to the current body of literature on collaboration by identifying how objects can help problematize issues and cement inter-organisational collaborations. Secondly it provides a novel account describing how two public sector organisations created a unique collaboration, despite pressing resistance from the regulatory authorities; and thirdly it extends Callon’s (1996) notion of problematization to examine how, what is rather vaguely described as ‘context’ in the literature, becomes enmeshed in decisions to collaborate. UNTIL 03/02/2016 THIS THESIS IS ONLY AVAILABLE FOR CONSULTATION AT ASTON UNIVERSITY LIBRARY WITH PRIOR ARRANGEMENT

Driving licensing renewal policy using neural network-based probabilistic decision support system

This paper investigates neural network-based probabilistic decision support system to assess drivers' knowledge for the objective of developing a renewal policy of driving licences. The probabilistic model correlates drivers' demographic data to their results in a simulated written driving exam (SWDE). The probabilistic decision support system classifies drivers' into two groups of passing and failing a SWDE. Knowledge assessment of drivers within a probabilistic framework allows quantifying and incorporating uncertainty information into the decision-making system. The results obtained in a Jordanian case study indicate that the performance of the probabilistic decision support systems is more reliable than conventional deterministic decision support systems. Implications of the proposed probabilistic decision support systems on the renewing of the driving licences decision and the possibility of including extra assessment methods are discussed.

Scalable harmonization of complex networks with local controllers

Computational and communication complexities call for distributed, robust, and adaptive control. This paper proposes a promising way of bottom-up design of distributed control in which simple controllers are responsible for individual nodes. The overall behavior of the network can be achieved by interconnecting such controlled loops in cascade control for example and by enabling the individual nodes to share information about data with their neighbors without aiming at unattainable global solution. The problem is addressed by employing a fully probabilistic design, which can cope with inherent uncertainties, that can be implemented adaptively and which provide a systematic rich way to information sharing. This paper elaborates the overall solution, applies it to linear-Gaussian case, and provides simulation results.