Doctoral Degrees (Information Science)

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Browsing Doctoral Degrees (Information Science) by browse.metadata.advisor "Kinghorn, Johann"

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    Incongruence and enactment in information systems : a sensemaking analysis
    (Stellenbosch : Stellenbosch University, 2013-12) Le Roux, Daniel Bartholomeus; Kinghorn, Johann; Stellenbosch University. Faculty of Arts and Social Sciences. Dept. of Information Science.
    ENGLISH ABSTRACT: In the six decades since organisations rst adopted computer machinery to support their operations this form of technology has undergone rapid evolution. This evolution is characterised by both the advancement of the machines themselves and the expansion of their application in the organisational domain through the development of increasingly advanced software. A particularly in uential development for large enterprises has been the introduction of computerised Enterprise Resource Planning Systems (ERPs) and the popularisation of proprietary ERP packages. By integrating the feature sets of an increasingly wide range of business software applications ERPs enable organisations to satisfy a large part of their information processing requirements by adopting a single software artefact. This approach o ers numerous bene ts to adopters as it ensures the integration of information processing activities across organisational functions. However, the realisation of these bene ts depends upon the organisation's ability to achieve congruence between its own structures and those embedded in proprietary ERP packages. This includes, on one level, the management of the processes of adaptation through which organisational actors become accustomed to a new technology and, on another level, the con guration and alignment of the artefact with the organisation's operating procedures. Despite the popularity of ERP adoption the achievement of congruence in information systems is an illusive ideal for many organisations. Accordingly, many Information Systems (IS) scholars have researched the organisational, technical and social factors which obstruct congruence and the interventions proposed to counter these. A key nding following from these investigations is that, notwithstanding the implementation of countering interventions, organisations often need to continue operations while experiencing some degree of incongruence or mis t in their information systems. The research performed in this study advances knowledge about this phenomenon by investigating the implications of incongruence for the behaviour of users of proprietary ERPs in organisations. Weickean Sensemaking Theory is adopted as conceptual framework to enable the investigation of instances of incongruence as events experienced by users in the context of their work environments. The theory dictates that users, rather than passively adopting the impositions of software artefacts, en- act information systems in unpredictable ways based on subjective and shared processes of sensemaking. An empirical investigation is performed and takes the form of a single, cross-sectional case study in which a variety of data collection techniques are utilised. The data sources are analysed and triangulated to trace the relationship between experiences of incongruence and patterns of information systems enactment among the user community. The ndings of the study reveal that experiences of incongruence cultivate knowledge sharing among a user community, a process which aligns their beliefs about the nature, role and use of a technology in an organisation. Furthermore, experiences of incongruence encourage users to augment designed technologies through the development informal information processing activities and alternative work ows. These forms of behaviour, while resolving users' experiences incongruence, lead to variance between the designed technology and the enacted technology creating various risks for the integrity of the organisation's business processes.
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    Knowledge discovery and anomalies — towards a dynamic decision-making model for medical informatics
    (Stellenbosch : Stellenbosch University, 2018-03) Arndt, Heidi; Kinghorn, Johann; Stellenbosch University. Faculty of Arts and Social Sciences. Dept. of Information Science.
    ENGLISH SUMMARY : Worldwide healthcare has become a major concern for modern society, which is challenged to make quality care accessible and affordable to all. With a slowing world economy, rapidly rising medical costs and a better-informed customer base, governments and healthcare organisations are under pressure to deliver a product that focuses on quality care, transparent costs and an excellent patient experience. This requires well-informed and nimble operating and decision-making by healthcare organisations, putting pressure on the discipline of informatics within systems. In a comprehensive literature survey, it was found that healthcare organisations are organisations made up of a wide variety of subsystems operating in a complex environment. In addition, there are individualities that challenge the development of health information systems. Bisociative knowledge discovery, which is the creative discovery of previously unknown information from habitually incompatible domains, was introduced as an alternative tool to address the need for decision support in the healthcare sector. It was further found that information networks are a useful way to integrate data from habitually incompatible domains. Lastly, frequent pattern mining was identified as the machine learning tool for mining bisociations within information networks. A knowledge discovery framework for data-intensive research focusing on the field of biomedical informatics was developed in this study. Within this framework, data are represented as integrated, heterogeneous information networks, and machine learning algorithms are applied to the data with the explicit purpose of finding interconnectedness within these structures that can lead to bisociative knowledge discoveries. This framework was further developed into a knowledge discovery process model for bisociative knowledge discovery with a focus on the healthcare sector. The knowledge discovery process model for bisociative knowledge discovery was then applied in a case study which made use of the Nationwide Inpatient Sample data that forms part of the Healthcare Cost and Utilization Project. The case study successfully demonstrated the construction of habitually incompatible domains and their integration into a heterogeneous information network. Furthermore, it demonstrated the application of frequent pattern mining algorithms to extract subgraphs from the constructed information network. This was followed by the constructing of the extracted subgraphs as concept graphs with the purpose of visualising the results for further interpretation. At the end of this research it was concluded that:  The proposed explorative data mining method using bisociative knowledge discovery revealed unexpected, potentially interesting relationships within the constructed information network.  Modelling data from the healthcare sector as an information network allowed visual insights into the structure of the data, which supported the detection of novel insights that otherwise would not have been revealed.  Organisations operating in a complex environment can be successfully unpacked into rich layers of abstraction and the integration of these layers can be automated through computing.