Browsing by Subject "decision-making"

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    Open Access
    How can CA(SA) university students be better prepared for the SAICA training programme: A focus on relational and decision-making skills
    (2022) Kotze, Ruhan; Miller, Taryn
    Purpose: This study investigates the extent to which aspirant CA(SA)s perceive how the academic programme is, and should be, developing relational and decision-making (RDM) skills. There has been extensive criticism that these skills are insufficiently developed in accounting students, an unfortunate reality which, consequently, hinders their expected performance during the SAICA training programme. Research method: A questionnaire was distributed to 103 aspirant CA(SA)s (of which 44 responded) working at one of the largest audit firms globally and currently in their 1st to 3rd year of the SAICA training programme. The questionnaire consisted of three main questions focusing on the respondents' perception of the academic programme. Findings: The majority of the respondents perceive that almost all of the RDM skills are developed to an intermediate or advanced level during the academic programme. However, respondents also perceive that the academic programme should place greater focus on developing certain RDM skills, such as relationship-building; professional scepticism and teamwork, to an advanced level, to maximise performance during the training programme. Lastly, the case study method, a teaching method by academics, resulted in the highest response rate for assisting in developing RDM skills. Originality and value: The study is the first to research RDM skills development, as defined within the new SAICA Competency Framework, during the South African academic programme. Furthermore, the findings of this study could assist SAICA in identifying RDM skills development shortcomings and whether the academic or training programme providers should bear more responsibility for developing certain skills.
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    Open Access
    The democratisation of decision-makers in data-driven decision-making in a Big Data environment: The case of a financial services organisation in South Africa
    (University of Cape Town, 2020) Hassa, Ishmael; Tanner, Maureen; Brown, Irwin
    Big Data refers to large unstructured datasets from multiple dissimilar sources. Using Big Data Analytics (BDA), insights can be gained that cannot be obtained by other means, allowing better decision-making. Big Data is disruptive, and because it is vast and complex, it is difficult to manage from technological, regulatory, and social perspectives. Big Data can provide decision-makers (knowledge workers) with bottom-up access to information for decision-making, thus providing potential benefits due to the democratisation of decision-makers in data-driven decision-making (DDD). The workforce is enabled to make better decisions, thereby improving participation and productivity. Enterprises that enable DDD are more successful than firms that are solely dependent on management's perception and intuition. Understanding the links between key concepts (Big Data, democratisation, and DDD) and decision-makers are important, because the use of Big Data is growing, the workforce is continually evolving, and effective decision-making based on Big Data insights is critical to a firm's competitiveness. This research investigates the influence of Big Data on the democratisation of decision-makers in data-driven decision-making. A Grounded Theory Method (GTM) was adopted due to the scarcity of literature around the interrelationships between the key concepts. An empirical study was undertaken, based on a case study of a large and leading financial services organisation in South Africa. The case study participants were diverse and represented three different departments. GTM facilitates emergence of novel theory that is grounded in empirical data. Theoretical elaboration of new concepts with existing literature permits the comparison of the emergent or substantive theory for similarities, differences, and uniqueness. By applying the GTM principles of constant comparison, theoretical sampling and emergence, decision-makers (people, knowledge workers) became the focal point of study rather than organisational decision-making processes or decision support systems. The concentrate of the thesis is therefore on the democratisation of decision-makers in a Big Data environment. The findings suggest that the influence of Big Data on the democratisation of the decisionmaker in relation to DDD is dependent on the completeness and quality of the Information Systems (IS) artefact. The IS artefact results from, and is comprised of, information that is extracted from Big Data through Big Data Analytics (BDA) and decision-making indicators (DMI). DMI are contributions of valuable decision-making parameters by actors that include Big Data, People, The Organisation, and Organisational Structures. DMI is an aspect of knowledge management as it contains both the story behind the decision and the knowledge that was used to decide. The IS artefact is intended to provide a better and more complete picture of the decision-making landscape, which adds to the confidence of decision-makers and promotes participation in DDD which, in turn, exemplifies democratisation of the decisionmaker. Therefore, the main theoretical contribution is that the democratisation of the decisionmaker in DDD is based on the completeness of the IS artefact, which is assessed within the democratisation inflection point (DIP). The DIP is the point at which the decision-maker evaluates the IS artefact. When the IS artefact is complete, meaning that all the parameters that are pertinent to a decision for specific information is available, then democratisation of the decision-maker is realised. When the IS artefact is incomplete, meaning that all the parameters that are pertinent to a decision for specific information is unavailable, then democratisation of the decision-maker breaks down. The research contributes new knowledge in the form of a substantive theory, grounded in empirical findings, to the academic field of IS. The IS artefact constitutes a contribution to practice: it highlights the importance of interrelationships and contributions of DMI by actors within an organisation, based on information extracted through BDA, that promote decisionmaker confidence and participation in DDD. DMI, within the IS artefact, are critical to decision-making, the lack of which has implications for the democratisation of the decisionmaker in DDD. The study has uncovered the need to further investigate the extent of each actor's contribution (agency) to DMI, the implications of generational characteristics on adoption and use of Big Data and an in-depth understanding of the relationships between individual differences, Big Data and decision-making. Research is also recommended to better explain democratisation as it relates to data-driven decision-making processes.
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    Open Access
    Trajectories of Change in South Africa's Freshwater Fish Fauna
    (2025) Kajee, Mohammed; Griffiths, Charles; Shelton, Jeremy; Dallas, Helen; Pegg, Josephine
    In South Africa, freshwaters are the most threatened ecosystems in the country, facing pressure from water abstraction, land-use change, pollution, invasive species and climate change. These anthropogenic pressures are directly impacting the diversity, abundance, and survival of freshwater fauna and flora across the country. Freshwater fishes are recognised as South Africa's most threatened species group, with one third of species classified as threatened (Vulnerable, Endangered, Critically Endangered) by the International Union for the Conservation of Nature (IUCN). Understanding patterns of diversity, threat, invasion, and protection status are vital for management. However, access to reliable and comprehensive long-term data on fish distributions, are limited. This thesis aims to collect, collate, and analyse historic and current freshwater fish records for all fish species (native and non-native) known to occur in South Africa, to better understand distribution, diversity, and threat patterns in space and time. A comprehensive freshwater fish occurrence dataset for South Africa was compiled and uploaded to the Freshwater Biodiversity Information System (FBIS, freshwaterbiodiversity.org). An 18-month historic-data collation effort resulted in the accrual of 35 955 records of freshwater fish from South Africa spanning 194 years (1828–2022), that have since also been uploaded to the Global Biodiversity Information Facility (GBIF). Together with pre-existing GBIF records (24 861), a total of 60 837 freshwater fish records are thus now available for South Africa. The data show a marked decline in the number of native fish occurrence records over the last decade relative to the previous five decades. Conversely, the number of occurrences for non-native fishes increased over the past three decades. A breakdown of the data is provided for all native (n = 105) and non-native (n = 24) species as well as for all endemic (n = 40) and threatened (n = 27) species. Using occurrence records collected for this thesis, spatial patterns in distribution, diversity, threat, invasion, and protection status of freshwater fishes in South Africa were analysed. Few studies have undertaken such analyses at ecologically and politically appropriate spatial scales, largely because of limited access to comprehensive biodiversity data sets. Results show that record density varies spatially, at both primary catchment and provincial scales. The diversity of freshwater fishes also varied spatially: native species hotspots were identified at provincial level in the Limpopo, Mpumalanga and KwaZulu-Natal provinces, endemic species hotspots were identified in the Western Cape, and threatened species hotspots in the Western Cape, Mpumalanga, Eastern Cape, and KwaZulu-Natal. Non-native species distributions mirrored threatened species hotspots in the Western Cape, Mpumalanga, Eastern Cape, and KwaZulu-Natal. Some 47% of threatened species records fell outside of protected areas, and 38% of non-native species records fell within protected areas. Concerningly, 58% of the distribution ranges of threatened species were invaded by non-native species. A data breakdown is provided for each of South Africa's nine provinces including total number of records, and the numbers of native, non-native, endemic and threatened species. These data provide a much-needed update of the known status and distribution of freshwater fishes in the country. To support future spatial modelling of South Africa's freshwater fish species distributions, a robust and repeatable method for applying Species Distribution Models (SDMs) to freshwater fishes was developed. Specifically, this thesis sought to investigate whether SDMs could be applied to a wide range of freshwater fishes in South Africa and whether the inclusion of invasive species spatial data would result in improved model accuracy and performance. Using a Bayesian Additive Regression Trees (BART) algorithm, the distributions of three black bass (Micropterus salmoides, M. dolomieu, and M. punctulatus) species and two native species (Clarias gariepinus and Pseudobarbus burgi) were modelled. A total of 148 hydrological and 16 biological predictor variables were specifically developed for this chapter, along with the collation of 75 environmental predictor variables. The results demonstrated that, with appropriate data cleaning and using a statistically robust method for selecting predictor variables, model outputs performed well, with the resulting modelled distributions congruent with known distributions and historic descriptions of the species' presence. All models for invasive, native, and threatened species performed well, with AUC values ranging from 0.923–0.994 and TSS ranging from 0.701–0.940. Furthermore, model performance for P. burgi was not improved by the inclusion of biological predictors (invasive species). The AUC for the original P. burgi model (including only climatic, environmental, and hydrological predictors) was 0.994, whereas the adapted model (including both climatic, environmental, and hydrological predictors as well as biological predictors) was marginally lower, with an AUC value of 0.993. This indicates that the inclusion of invasive species as biological predictors may not be necessary to produce accurate predicted species distributions. This thesis also presents a case study demonstrating how the FBIS system was used to provide freshwater fish spatial data products to a national conservation decision-support tool – The Department of Forestry, Fisheries, and the Environment (DFFE) National Environmental Screening Tool. The Tool uses empirical and modelled biodiversity data to guide Environmental Impact Assessments of proposed developments. Occurrence records for 34 threatened freshwater fishes occurring in South Africa were extracted from the FBIS and verified by taxon specialists, resulting in 6 660 records being used to generate modelled and empirical national distribution layers. This represents the first inclusion of freshwater biodiversity data in the Screening Tool, and future iterations of the Tool will incorporate additional freshwater taxa. This case study demonstrates how systems like the FBIS can play a pivotal role in the data-to-decision pipeline through supporting data-driven conservation and management decisions at a national level. The collective findings of the thesis are concluded in Chapter 6, which highlight the importance of collecting, collating, and consolidating biodiversity data at a national level. The dataset presents the largest (by number of records) collection of freshwater fish records in Africa to date, with data spanning 194 years. This thesis also presents an updated version of South Africa's Species List for Freshwater Fishes, which includes all known native (n = 107) and non-native (n = 27) species occurring in the country, and key species attributes like species origin, endemism, and threat status. By consolidating historical data for all known freshwater fishes, this thesis presents the first comprehensive assessment of the status of South Africa's freshwater fish fauna, providing valuable insights into spatial patterns of richness, endemism, threat status and non-native species, as well as assessing how effective the country's protected area network is for protecting freshwater fishes. This thesis addressed the critical need for a deeper, data-driven understanding of spatial and temporal variation in South Africa's freshwater fishes – an essential foundation for sound management of these species and their habitats into the future.