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- Byungura, Jean Claude, 1979-
(författare)
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Improving IT Integration for Higher Education Institutional Performance : Towards a Contextualised IT-Institutional Alignment Model
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The integration of information technology (IT) into service delivery is currently seen as an innovative strategy to support the modernising of universities worldwide. However, in some institutions in developing countries, including Rwanda, IT has failed to add the intended value to university services, despite huge associated investments in IT. Consequently, IT-organisational alignment continues to be a primary concern for university managers. This alignment is viewed in terms of its strategic, socio-cultural, and technological dimensions. For effective IT-institutional alignment, several antecedents (alignment practices) for creating an appropriate fit between IT and organisations have been suggested in the literature. However, several studies exploring IT alignment focused mainly on general business companies, and similar research with an emphasis on higher education institutions is still scarce. Therefore, the aim of this research was twofold: firstly, it attempted to understand the process of IT integration into universities; and secondly, to propose a contextual model for IT-institutional alignment within a higher education context. A design science research methodology (DSRM) was applied in this research, using surveys and case studies as research strategies. Preliminary findings at the exploration phase of this research indicated a strong misalignment between IT and the university services caused by the lack of clearly defined alignment practices. Furthermore, as the research main outcome, an IT-Institutional Alignment Model (ITIAM) was proposed after reaching an understanding of the current state and challenges related to IT integration into teaching, learning, research and university administration. This model includes 44 alignment practices, related to both technical and non-technical dimensions. These alignment practices were clustered under six categories: (1) Communication, (2) Structure/Governance, (3) Technology Scope, (4) Competence/Value Measurement, (5) Skills, and (6) Partnership. Alignment practices related to institutional structure and governance, skills and communication were found to have a strong positive influence on the institutional performance, as compared to those related to competence and value measurement, partnership, and technology scope. Based on the research findings, the proposed ITIAM, which was iteratively tested and evaluated using case study institutions, was found to be a relevant tool for guiding the implementation of IT systems towards the improvement of institutional performance. Hence, this thesis makes a theoretical contribution by applying the concept of IT alignment within a higher education context and by documenting the empirically tested contextual alignment practices as conveyed in the ITIAM Model. Additionally, as a practical implication, the results can serve as a reference for an effective IT integration process in university services and for how to improve performance through effective use of IT in teaching, learning, research and educational management.
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| 2. |
- Abdalmoaty, Mohamed, 1986-
(författare)
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Learning Stochastic Nonlinear Dynamical Systems Using Non-stationary Linear Predictors
- 2017
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The estimation problem of stochastic nonlinear parametric models is recognized to be very challenging due to the intractability of the likelihood function. Recently, several methods have been developed to approximate the maximum likelihood estimator and the optimal mean-square error predictor using Monte Carlo methods. Albeit asymptotically optimal, these methods come with several computational challenges and fundamental limitations.The contributions of this thesis can be divided into two main parts. In the first part, approximate solutions to the maximum likelihood problem are explored. Both analytical and numerical approaches, based on the expectation-maximization algorithm and the quasi-Newton algorithm, are considered. While analytic approximations are difficult to analyze, asymptotic guarantees can be established for methods based on Monte Carlo approximations. Yet, Monte Carlo methods come with their own computational difficulties; sampling in high-dimensional spaces requires an efficient proposal distribution to reduce the number of required samples to a reasonable value.In the second part, relatively simple prediction error method estimators are proposed. They are based on non-stationary one-step ahead predictors which are linear in the observed outputs, but are nonlinear in the (assumed known) input. These predictors rely only on the first two moments of the model and the computation of the likelihood function is not required. Consequently, the resulting estimators are defined via analytically tractable objective functions in several relevant cases. It is shown that, under mild assumptions, the estimators are consistent and asymptotically normal. In cases where the first two moments are analytically intractable due to the complexity of the model, it is possible to resort to vanilla Monte Carlo approximations. Several numerical examples demonstrate a good performance of the suggested estimators in several cases that are usually considered challenging.
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