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dc.contributor.advisorMaharaj, Manoj Sewak.
dc.creatorParbanath, Steven.
dc.date.accessioned2018-10-22T06:31:31Z
dc.date.available2018-10-22T06:31:31Z
dc.date.created2014
dc.date.issued2014
dc.identifier.urihttp://hdl.handle.net/10413/15698
dc.descriptionDoctor of Philosophy in Information Systems and Technology. University of KwaZulu-Natal, Durban 2014.en_US
dc.description.abstractUniversities generally use a human-intensive approach such as peer evaluations, expert judgments, group interviews or a weighting system to estimate academic productivity. This study develops an algorithmic approach by integrating the fuzzy Multi-Criteria Decision Making (MCDM) and the fuzzy TOPSIS methods to estimate productivity of academic staff at tertiary institutions. Currently, evaluations are done in the conventional manner and as a result, the outputs are difficult to quantify. There are no standard methods in evaluating the outputs and the estimates are therefore hard to validate. It is therefore suggested that a data intensive approach (also referred to as algorithmic approach) be adopted. An algorithmic approach is empirical and will produce results that are easily quantifiable. The algorithmic approach allows for the IS Principles of data collection, processing, analysis and interpretation to be easily applied. If an algorithmic approach were adopted, it would generally revolve around the numeric-value approach, which produces a precise measure of productivity. Recently however, the software engineering domain had to also consider non-numeric attributes (also referred to as linguistic expressions) such as very low, low, high and very high for productivity estimation (Odeyale et al., 2014). The imprecise nature of these attributes constitutes uncertainty in their interpretation and therefore could not be measured or quantified appropriately in the past. It is now possible to do so using fuzzy logic and fuzzy sets. Since academic departments are constantly faced with imprecision and uncertainty, an algorithmic fuzzy-based decision model is the most suitable approach that can be used to estimate productivity. The nature of duties performed by academic staff lends itself more efficiently to a qualitative rather than a quantitative evaluation (Chaudhari et al., 2012). These qualitative evaluations are reliant on human judgment and could be described using linguistic expressions such as weak, average, good and excellent (Khan et al., 2011). In this study, a fuzzy MCDM method called Fuzzy Analytic Hierarchy Process (FAHP) is used to estimate productivity of academic staff. Choosing the most preferred alternative, ranking and selection will be done using the fuzzy TOPSIS method. The Design Science Research Methodology (DSRM) was used to develop a fuzzy-based productivity estimation system using these two methods. The results of the study indicated that the fuzzy-based system produced results that were more reliable than conventional methods. Future research should however explore how fuzzy logic and fuzzy set theory could be integrated into other productivity estimation techniques such as the DEA and SAW models.en_US
dc.language.isoen_ZAen_US
dc.subjectTheses - Information Systems and Technology.en_US
dc.subject.otherFuzzy analytic.en_US
dc.subject.otherTopsis.en_US
dc.subject.otherProductivity.en_US
dc.subject.otherMulti-criteria decision making (MCDM)en_US
dc.titleComputer-based productivity estimation of academic staff using the fuzzy analytic hierarchy process and fuzzy topsis method.en_US
dc.typeThesisen_US


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