To reveal is to critique:actor-network theory and critical information systems research

It has been suggested that, in order to maintain its relevance, critical research must develop a strong emphasis on empirical work rather than the conceptual emphasis that has typically characterized critical scholarship in management. A critical project of this nature is applicable in the information systems (IS) arena, which has a growing tradition of qualitative inquiry. Despite its relativist ontology, actor–network theory places a strong emphasis on empirical inquiry and this paper argues that actor–network theory, with its careful tracing and recording of heterogeneous networks, is well suited to the generation of detailed and contextual empirical knowledge about IS. The intention in this paper is to explore the relevance of IS research informed by actor–network theory in the pursuit of a broader critical research project as de? ned in earlier work.

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

Neural network enhanced self tuning adaptive control application for non-linear control of dynamic systems

The main theme of research of this project concerns the study of neutral networks to control uncertain and non-linear control systems. This involves the control of continuous time, discrete time, hybrid and stochastic systems with input, state or output constraints by ensuring good performances. A great part of this project is devoted to the opening of frontiers between several mathematical and engineering approaches in order to tackle complex but very common non-linear control problems. The objectives are: 1. Design and develop procedures for neutral network enhanced self-tuning adaptive non-linear control systems; 2. To design, as a general procedure, neural network generalised minimum variance self-tuning controller for non-linear dynamic plants (Integration of neural network mapping with generalised minimum variance self-tuning controller strategies); 3. To develop a software package to evaluate control system performances using Matlab, Simulink and Neural Network toolbox. An adaptive control algorithm utilising a recurrent network as a model of a partial unknown non-linear plant with unmeasurable state is proposed. Appropriately, it appears that structured recurrent neural networks can provide conveniently parameterised dynamic models for many non-linear systems for use in adaptive control. Properties of static neural networks, which enabled successful design of stable adaptive control in the state feedback case, are also identified. A survey of the existing results is presented which puts them in a systematic framework showing their relation to classical self-tuning adaptive control application of neural control to a SISO/MIMO control. Simulation results demonstrate that the self-tuning design methods may be practically applicable to a reasonably large class of unknown linear and non-linear dynamic control systems.

Curvature-driven smoothing: a learning algorithm for feed-forward networks

The performance of feed-forward neural networks in real applications can be often be improved significantly if use is made of a-priori information. For interpolation problems this prior knowledge frequently includes smoothness requirements on the network mapping, and can be imposed by the addition to the error function of suitable regularization terms. The new error function, however, now depends on the derivatives of the network mapping, and so the standard back-propagation algorithm cannot be applied. In this paper, we derive a computationally efficient learning algorithm, for a feed-forward network of arbitrary topology, which can be used to minimize the new error function. Networks having a single hidden layer, for which the learning algorithm simplifies, are treated as a special case.

Training with noise is equivalent to Tikhonov regularization

It is well known that the addition of noise to the input data of a neural network during training can, in some circumstances, lead to significant improvements in generalization performance. Previous work has shown that such training with noise is equivalent to a form of regularization in which an extra term is added to the error function. However, the regularization term, which involves second derivatives of the error function, is not bounded below, and so can lead to difficulties if used directly in a learning algorithm based on error minimization. In this paper we show that, for the purposes of network training, the regularization term can be reduced to a positive definite form which involves only first derivatives of the network mapping. For a sum-of-squares error function, the regularization term belongs to the class of generalized Tikhonov regularizers. Direct minimization of the regularized error function provides a practical alternative to training with noise.

After ANT:an illustrative discussion of the implications for qualitative accounting case research

The main aim of this article is to shed some light on the way in which actor network theory (ANT) might contribute to case research in accounting. The paper will seek to explain some of the theoretical suppositions which are commonly associated with ANT and which have so far made little impact on the accounting literature. At the same time the accounting literature has shown a particular reluctance to engage with the central concept of ANT which Lee and Hassard characterise as the desire to bring together the "human and non-human, social and technical factors in the same analytical view". The article also features a discussion of a research project which used an approach giving emphasis to both humans and objects in order to understand how ``facts'' have come to be settled as they are. In taking such views into the research it is hoped to provide insight into both the detail of accounting as it is practised within organisations and the manner in which human actors and objects of technology may combine to constitute networks within organisations.

Casemix accounting systems and medical coding:organisational actors balanced on "leaky black boxes"

The adoption of DRG coding may be seen as a central feature of the mechanisms of the health reforms in New Zealand. This paper presents a story of the use of DRG coding by describing the experience of one major health provider. The conventional literature portrays casemix accounting and medical coding systems as rational techniques for the collection and provision of information for management and contracting decisions/negotiations. Presents a different perspective on the implications and effects of the adoption of DRG technology, in particular the part played by DRG coding technology as a part of a casemix system is explicated from an actor network theory perspective. Medical coding and the DRG methodology will be argued to represent ``black boxes''. Such technological ``knowledge objects'' provide strong points in the networks which are so important to the processes of change in contemporary organisations.

Accounting information systems as knowledge objects:some effects of objectualization

This paper will outline a research methodology informed by theorists who have contributed to actor network theory (ANT). Research informed from such a perspective recognizes the constitutive role of accounting systems in the achievement of broader social goals. Latour, Knoor Cetina and others argue that the bringing in of non-human actants, through the growth of technology and science, has added immeasurably to the complexity of modern society. The paper ‘sees’ accounting and accounting systems as being constituted by technological ‘black boxes’ and seeks to discuss two questions. One concerns the processes which surround the establishment of ‘facts’, i.e. how ‘black boxes’ are created or accepted (even if temporarily) within society. The second concerns the role of existing ‘black boxes’ within society and organizations. Accounting systems not only promote a particular view of the activities of an organization or a subunit, but in their very implementation and operation ‘mobilize’ other organizational members in a particular direction. The implications of such an interpretation are explored in this paper. Firstly through a discussion of some of the theoretic constructs that have been proposed to frame ANT research. Secondly an attempt is made to relate some of these ideas to aspects of the empirics in a qualitative case study. The case site is in the health sector and involves the implementation of a casemix accounting system. Evidence from the case research is used to exemplify aspects of the theoretical constructs.

A biography:fabrications in the life of an ERP package

This paper provides an account of the way Enterprise Resource Planning (ERP) systems change over time. These changes are conceptualized as a biographical accumulation that gives the specific ERP technology its present character, attributes and historicity. The paper presents empirics from the implementation of an ERP package within an Australasian organization. Changes to the ERP take place as a result of imperatives which arise during the implementation. Our research and evidence then extends to a different time and place where the new release of the ERP software was being 'sold' to client firms in the UK. We theorize our research through a lens based on ideas from actor network theory (ANT) and the concept of biography. The paper seeks to contribute an additional theorization for ANT studies that places the focus on the technological object and frees it from the ties of the implementation setting. The research illustrates the opportunistic and contested fabrication of a technological object and emphasizes the stability as well as the fluidity of its technologic. Copyright © 2007 SAGE.

Cutting the Gordian knot [?]:a response to Lukka and Vinnari (2014)

Purpose – The purpose of this paper is to constructively discuss the meaning and nature of (theoretical) contribution in accounting research, as represented by Lukka and Vinnari (2014) (hereafter referred to as LV). The authors aim is to further encourage debate on what constitutes management accounting theory (or theories) and how to modestly clarify contributions to the extant literature. Design/methodology/approach – The approach the authors take can be seen as (a)n interdisciplinary literature sourced analysis and critique of the movement’s positioning and trajectory” (Parker and Guthrie, 2014, p. 1218). The paper also draws upon and synthesizes the present authors and other’s contributions to accounting research using actor network theory. Findings – While a distinction between domain and methods theories … may appear analytically viable, it may be virtually impossible to separate them in practice. In line with Armstrong (2008), the authors cast a measure of doubt on the quest to significantly extend theoretical contributions from accounting research. Research limitations/implications – Rather than making (apparently) grandiose claims about (theoretical) contributions from individual studies, the authors suggest making more modest claims from the research. The authors try to provide a more appropriate and realistic approach to the appreciation of research contributions. Originality/value – The authors contribute to the debate on how theoretical contributions can be made in the accounting literature by constructively debating some views that have recently been outlined by LV. The aim is to provide some perspective on the usefulness of the criteria suggested by these authors. The authors also suggest and highlight (alternative) ways in which contributions might be discerned and clarified.

Virtuous or vicious cycle? Inscribing diverse professional values in lecture capture systems

This paper examines how the introduction and use of a new information system affects and is affected by the values of a diverse professional workforce. It uses the example of lecture capture systems in a university. Its contribution is to combine two concepts taken from actor-network theory, namely accumulation and inscription, and combine them with an integrated framework of diversity management. A model is developed of accumulation cycles in lecture capture usage, involving multiple interacting actants, including the broader environment, management commitment to diversity, work group characteristics, individual practices and the affordances of technology. Using this model, alternative future inscriptions can be identified - an optimal one, which enhances professional values, as a result of a virtuous accumulation cycle, or a sub-optimal one, as a result of a vicious cycle. It identifies diversity management as an important influence on how professional values are enhanced, modified or destroyed.

The challenge of real-time automatic mapping for environmental monitoring network management

The automatic interpolation of environmental monitoring network data such as air quality or radiation levels in real-time setting poses a number of practical and theoretical questions. Among the problems found are (i) dealing and communicating uncertainty of predictions, (ii) automatic (hyper)parameter estimation, (iii) monitoring network heterogeneity, (iv) dealing with outlying extremes, and (v) quality control. In this paper we discuss these issues, in light of the spatial interpolation comparison exercise held in 2004.

Robust automatic mapping algorithms in a network monitoring scenario

Automatically generating maps of a measured variable of interest can be problematic. In this work we focus on the monitoring network context where observations are collected and reported by a network of sensors, and are then transformed into interpolated maps for use in decision making. Using traditional geostatistical methods, estimating the covariance structure of data collected in an emergency situation can be difficult. Variogram determination, whether by method-of-moment estimators or by maximum likelihood, is very sensitive to extreme values. Even when a monitoring network is in a routine mode of operation, sensors can sporadically malfunction and report extreme values. If this extreme data destabilises the model, causing the covariance structure of the observed data to be incorrectly estimated, the generated maps will be of little value, and the uncertainty estimates in particular will be misleading. Marchant and Lark [2007] propose a REML estimator for the covariance, which is shown to work on small data sets with a manual selection of the damping parameter in the robust likelihood. We show how this can be extended to allow treatment of large data sets together with an automated approach to all parameter estimation. The projected process kriging framework of Ingram et al. [2007] is extended to allow the use of robust likelihood functions, including the two component Gaussian and the Huber function. We show how our algorithm is further refined to reduce the computational complexity while at the same time minimising any loss of information. To show the benefits of this method, we use data collected from radiation monitoring networks across Europe. We compare our results to those obtained from traditional kriging methodologies and include comparisons with Box-Cox transformations of the data. We discuss the issue of whether to treat or ignore extreme values, making the distinction between the robust methods which ignore outliers and transformation methods which treat them as part of the (transformed) process. Using a case study, based on an extreme radiological events over a large area, we show how radiation data collected from monitoring networks can be analysed automatically and then used to generate reliable maps to inform decision making. We show the limitations of the methods and discuss potential extensions to remedy these.

GTM: the generative topographic mapping

This thesis describes the Generative Topographic Mapping (GTM) --- a non-linear latent variable model, intended for modelling continuous, intrinsically low-dimensional probability distributions, embedded in high-dimensional spaces. It can be seen as a non-linear form of principal component analysis or factor analysis. It also provides a principled alternative to the self-organizing map --- a widely established neural network model for unsupervised learning --- resolving many of its associated theoretical problems. An important, potential application of the GTM is visualization of high-dimensional data. Since the GTM is non-linear, the relationship between data and its visual representation may be far from trivial, but a better understanding of this relationship can be gained by computing the so-called magnification factor. In essence, the magnification factor relates the distances between data points, as they appear when visualized, to the actual distances between those data points. There are two principal limitations of the basic GTM model. The computational effort required will grow exponentially with the intrinsic dimensionality of the density model. However, if the intended application is visualization, this will typically not be a problem. The other limitation is the inherent structure of the GTM, which makes it most suitable for modelling moderately curved probability distributions of approximately rectangular shape. When the target distribution is very different to that, theaim of maintaining an `interpretable' structure, suitable for visualizing data, may come in conflict with the aim of providing a good density model. The fact that the GTM is a probabilistic model means that results from probability theory and statistics can be used to address problems such as model complexity. Furthermore, this framework provides solid ground for extending the GTM to wider contexts than that of this thesis.

Multi-valued control problems and mixture density network

We have proposed a novel robust inversion-based neurocontroller that searches for the optimal control law by sampling from the estimated Gaussian distribution of the inverse plant model. However, for problems involving the prediction of continuous variables, a Gaussian model approximation provides only a very limited description of the properties of the inverse model. This is usually the case for problems in which the mapping to be learned is multi-valued or involves hysteritic transfer characteristics. This often arises in the solution of inverse plant models. In order to obtain a complete description of the inverse model, a more general multicomponent distributions must be modeled. In this paper we test whether our proposed sampling approach can be used when considering an arbitrary conditional probability distributions. These arbitrary distributions will be modeled by a mixture density network. Importance sampling provides a structured and principled approach to constrain the complexity of the search space for the ideal control law. The effectiveness of the importance sampling from an arbitrary conditional probability distribution will be demonstrated using a simple single input single output static nonlinear system with hysteretic characteristics in the inverse plant model.