Theses and dissertations (Accounting and Informatics)

Permanent URI for this collectionhttp://ir-dev.dut.ac.za/handle/10321/4

Browse

Now showing 1 - 20 of 238

The impact of financial statement quality and firm characteristics on access to finance by small and medium-sized enterprises in eThekwini
(2024) Mayendisa, Qiniso Prince; Stainbank, Lesley June; Ramsarghey, Anchal
The South African government has established several public sector institutions that cater to small and medium-sized entities’ (SMEs) needs through the Department of Trade, Industries and Competition. These institutions, known as Business Development Service Providers, assist SMEs in running their businesses more effectively and can enhance access to finance as an alternative form of collateral in circumstances where security for a loan is required. However, most SMEs apply for finance from financial institutions. As access to finance has been identified as a major obstacle limiting the growth and survival of SMEs, the objective of this research was to investigate the impact of financial statement quality and firm characteristics on access to finance by SMEs in eThekwini using the “Applied and Received” approach. The main research objective was divided into three sub-objectives; these were to determine the forms of finance being used by SMEs, to determine the accounting frameworks being used by SMEs, and to investigate whether financial statement quality and firm characteristics affect their access to finance. To achieve these objectives, a questionnaire was administered to owners of SMEs in eThekwini. The results revealed that the main forms of finance used by the SMEs were overdraft facilities, bank loans, factoring, leasing, and hire purchase, and that the average rate of extent of access to finance is 19.10%. The findings also revealed that 4.8% of the respondents were using IFRS, 72.9% were using IFRS for SMEs, and 22.3% were using South African Statements of Generally Accepted Accounting Practice. Lastly, the findings revealed that firm age, firm size, collateral, and financial statement quality have a significant effect on access to finance by SMEs. Therefore, possession of such firm characteristics and financial statement quality are important predictors of SMEs’ successful access to finance. The Government needs to help SMEs by providing them with educational programs that will assist them in compiling and understanding their financial statements to keep them improving and surviving. Furthermore, an SME’s growth and survival also depends on its access to finance.
Data mining and machine learning : a study of the CO2 emission trends in South Africa
(2024) Mohamed, Ghulam Masudh; Patel, Sulaiman Saleem; Naicker, Nalindren
This study addresses the pressing global issue of elevated carbon dioxide emissions (CO2E), with a particular focus on South Africa (SA), which ranks amongst the world's top emitters and largest in Africa. By introducing a novel integration of Change-point Analysis (CPA) and Machine Learning (ML) techniques, this research addresses significant gaps in CO2E trend analysis. Unlike previous studies, this research applies CPA methodologies within the distinct context of SA, employing algorithms like cumulative sum (CUSUM) and Bootstrap analysis to pinpoint crucial change-points in CO2E data specific to the country. The Bootstrap analysis determines the confidence levels associated with each detected change. Additionally, this study sought to validate historical trends and predict future patterns using ML models, with a specific focus on employing the AdaBoost ensemble learning technique. Drawing on insights from a Preferred Reporting Items for Systematic Reviews and MetaAnalyses (PRISMA)-based systematic review, the research selects input variables based on the factors identified as significant contributors to CO2E, ensuring the models capture the relevant variables effectively. The results of the systematic review highlight energy production and economic growth as key drivers of CO2E, thus validating their selection as input data for constructing the CPA and ML models. To conduct this study, secondary data was obtained from the World Bank's Open Data initiative data repository, a common source for environmental research. This selection was justified by a literature review, which highlighted the reliability and applicability of this data source. The CPA results reveal significant change-points in electricity generation, economic growth, and CO2E, with an average confidence level of 94%, indicating the accuracy of this analytical approach. Moreover, the CPA results emphasise the relationship between economic growth, electricity production, and CO2E in SA. Before forecasting future CO2E trends, the effectiveness of the AdaBoost regressor in enhancing model performance was benchmarked against traditional ML algorithms, including Linear regression, Polynomial regression, Bayesian Linear regression and K-Nearest Neighbors (KNN) regression, to determine the most effective technique for forecasting CO2E. The researcher evaluated model performance using key regression ML performance metrics, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), coefficient of determination (R2) score, and an additional accuracy score introduced by the researcher. Notably, the AdaBoost models demonstrated superior performance, with an average RMSE score of 10,143.17 kilotons (kt), MAE score of 9,642.64 kt, R2 of 0.90, and accuracy of 96.74%. The study also revealed that, on average, models that were trained using the AdaBoost algorithm surpassed traditional ML models, in terms of performance. They achieved a reduction in RMSE score by 6,417.29 kt, a decrease in MAE score by 4,358.09 kt, an increase in R2 score by 0.07 and enhanced accuracy by 0.60%. Additionally, a comparative analysis of the repeated holdout methods and cross-validation techniques was conducted, with results revealing that repeated holdout had a more significant impact on model performance. After excluding outliers, the average improvement in crossvalidation results, due to the repeated holdout method, was a decrease of 783.32 kt for RMSE, a reduction of 1,289.39 kt for MAE, and an increase of 0.88% for accuracy. The extent to which the repeated holdout method improved the performance of ML models that were integrated with cross-validation techniques, was correlated with the initial model performance. For ML models with RMSE and MAE scores equal to or exceeding 15,000 kt, the findings indicate that the repeated holdout methods studied should enhance performance by at least 2,000 kt. Similarly, an improvement of nearly 3% or higher in accuracy was noted, when the crossvalidation value for this metric was 94% or lower. The AdaBoost model, integrated with repeated holdout, was selected as the optimal model, as evidenced by the results, for forecasting CO2E in SA from 2021 to 2027. The forecasted CO2E trends validate that energy production and economic growth are indeed the primary drivers of CO2E in SA, as previously highlighted by the CPA model. This underscores the importance of addressing these factors to effectively mitigate carbon emissions in the country. Moreover, the forecasted results indicate that SA is unlikely to meet the global temperature limit of 1.5 degrees Celsius by 2030, given the trajectory showing a shortfall in achieving the target level of 334 million tonnes (Mt) of CO2E, agreed upon in the Paris Agreement. However, the country did meet its CO2E commitments outlined in the 2030 National Development Plan, showing some progress towards environmental sustainability. Nonetheless, the failure to meet these targets at their lower ranges suggests the need for further efforts to reduce carbon emissions, which is crucial for aligning with the Paris Agreement objectives and achieving a zero net emission rate by 2050. This highlights the importance of ongoing initiatives to enhance environmental policies and practices in SA. Future research should focus on integrating load-shedding dynamics into the analysis to examine and confirm its effects on energy production, economic growth, and CO2E in SA. Additionally, future research should focus on forecasting future change-points for the socio-economic indicators or variables utilised in this study. This can help policymakers anticipate fluctuations and devise proactive strategies, to address environmental and economic challenges effectively. It is also recommended that future research consider the output of renewable energy production, when analysing CO2E trends.
Health insurance cross-selling predictions with machine learning for South African consumers
(2024) Mavundla, Khulekani; Thakur, Surendra
Cross-selling is the practice of selling additional products or services to an existing customer to increase business revenue. Cross-selling health insurance is challenging for companies, as they spend significant time meeting with prospective clients without knowing the likelihood of a sale. A health insurance provider often markets additional insurance products to its clients through different channels. This study aims to develop a robust ML model to help health insurance companies identify potential customers likely to engage in cross-selling. Objectives include extracting and preparing customer data from a large South African insurance company using suitable ML techniques. The study also seeks to determine effective algorithms for predicting health insurance cross-selling and to identify influential features for algorithm selection. This study adopted a quantitative research approach focused on extracting health insurance customer data. To achieve this, the study applied ML techniques by using the Python language using a dataset obtained from a large South African insurance company which is a rich repository that contains demographics, health conditions, and policy information. The study applied various ML algorithms, including Random Forest, KNearest Neighbors, XGBoost classifier, and Logistic Regression, feature engineering techniques were employed to enhance predictive accuracy. Analyzing 1,000,000 customer records with 17 features, Random Forest emerged as the top model with an accuracy of 0.91 and an F1 score of 1.00. The study found that customers aged 2570, with prior insurance and longer service history, are more likely to purchase additional health insurance. This study will assist insurance providers in developing a strategy for reaching out to those clients in order to enhance their business operations and revenue.
Determinants of participation of Msunduzi local municipality’s peri-urban households in the digital finance economy
(2024) Nyide, Nelisiwe Fortunate; Olarewaju, Odunayo Magret
Digital finance is an instrument that has the potential of improving access to finance to underprivileged groups such as peri-urban communities. Digital financial tools are capable of achieving traditional crisis management objectives with greater potency and accuracy than was historically possible. The financial services sector is in a process of accelerating change by adopting new business models based on convergent technological developments to increase customer participation in periurban areas. Therefore, the financial services sector can use digital finance to improve the availability of household financial services through diversified financial products, thus promoting the growth of household consumption. However, several studies maintain that determinants of digital finance participation of marginalised households, including peri-urban households, are generally underdetermined. Moreover, there is limited literature on the participation of South African households in the digital economy. Scholarly literature asserts that the level of participation of South Africans in digital finance is concerning. This is largely due to a lack of awareness and knowledge of digital financial services that are available to South African households. This study seeks to bridge that gap by examining the determinants of participation of peri-urban households in digital finance in the financial services sector in KwaZuluNatal, South Africa. A quantitative research approach was adopted to answer the research questions. This method was found to be suitable for this study given that the research objectives can be best measured using a structured survey that is quantitative in nature. The target population of this study consisted of peri-urban households located in the Greater Edendale area, which is the largest peri-urban area within the Msunduzi Local Municipality. The sample size for this study was 384 periurban households which were selected using purposive sampling, derived from nonprobability sampling. The questionnaires were in English and were also translated into isiZulu in order to make it easier for respondents to participate in this study. The Statistical Package for the Social Sciences (SPSS) was used to compile the descriptive statistics. The results of this study indicate that the general public in economically disadvantaged communities participates in digital financial transactions in the financial services sector on a regular basis. A Spearman correlation analysis found a substantial positive link between the usage of digital platforms by peri-urban families and their degree of participation in digital finance. This association was shown to be statistically significant (r = .649, n = 315, p < .001). However, the results of a Mann-Whitney U test showed that there was no statistically significant difference between genders with regard to involvement in digital finance (Z = -1.804, p = .071). A correlation analysis was undertaken to determine whether peri-urban households’ awareness of digital financial services influenced their adoption of digital platforms. The Spearman correlation analysis (r = .768, n = 315, p < .001) showed a strong and significant relationship between peri-urban households’ knowledge and awareness of digital financial services and their use of digital platforms. Additionally, a Spearman correlation analysis (r = -.524, n = 315, p < .001) revealed a significant negative association between peri-urban households’ adoption of digital financial platforms and their digital literacy. This is despite the fact that literature argues that in South Africa, the adoption of digital financial services is negatively affected by a lack of information and knowledge which is prevalent among marginalised communities.
Evolving a framework to observe and analyse customer experience on the Twitter platform using machine learning techniques
(2024) Moodley, Thaneshni; Thakur, Surendra
Retailers have become more focused on retaining and turning existing customers into longterm clients because retailers have become more competitive, customers more demanding, and competitors more aggressive. The 2020 COVID-19 pandemic has forced a transformation for retailers. Within months, a revolution has taken place, constituting major changes to how consumers view cash, how they shop online and what they expect from retailers as part of a positive buying experience. Consumers increasingly expect retailers to create a seamless customer experience. This often means leaning on digital capabilities to create a seamless, omni-channel experience by linking different aspects of the customer shopping experience. The usage of big data analytics has primarily been implemented outside of South Africa to better understand customer connections and experiences, highlighting a noticeable research gap in South Africa. It has been proven to be an effective tool for retailers in predicting customer behaviour. There is a need to reduce the complexities in understanding which are the most appropriate machine learning techniques for sentiment analysis of online customer experience and to capitalise on development. Thereafter, online retailers are better equipped to tailor machine learning tools to craft analytical tools. Given the massive migration to online transactions, this work presents a rigorous analysis of social media posts, which is paramount for modern-era retailers. Businesses can use sentiment analysis to determine how well their brand is performing in the marketplace, learn more about the attitudes of their customers and determine whether their items receive more positive or negative feedback. A longitudinal study was adopted to analyse a dataset of retail-related tweets for the identification of customer complaints using a sentiment analysis hybrid approach, which is a combination of lexicon and machine learning approaches. A conceptual framework was developed to observe and analyse customer experiences on the Twitter platform using machine learning techniques. The framework encompasses components such as data preparation, natural language processing pre-processing techniques, calculating sentiment using sentiment lexicon and ML techniques, and thereafter a selection of the best-performing machine learning technique for sentiment analysis within the developed conceptual framework. The extracted dataset contains 240 000 tweets posted between 01 January 2017 and 31 January 2019, out of which 27 233 tweets were selected for the study. Natural language pre-processing techniques were applied to the dataset, including tokenisation, stemming, lemmatisation, part-of-speech tagging, and name-of-entity recognition. Supervised and deep machine learning gave the best results of 61.75 and 60.25. This study has identified deep learning as a good technique for sentiment analysis when NLP pre-processing methods are done in a certain order. A study on analysing retail complaints posted on the Twitter platform using a sentiment analytic framework has not been done in South Africa before. This study has proven that the sentiment analysis hybrid approach is highly capable of analysing social media data.
The inclination to pursue fashion and beauty digital entrepreneurship amongst selected final year Diploma students in a South African university
(2024) Maphanga, Ezile; Moyane, Smangele Pretty; Nkomo, Ntando
In the age of digital transformation, the inclination to pursue digital entrepreneurship has become rampant, bringing about a broader acceptance of the idea of conducting business online especially by young people. In that regard, entrepreneurs are seizing the opportunity through digital entrepreneurship, with the fashion and beauty industry being a prominent sector for online business. Despite the growth in online fashion and beauty trading, there is limited research and understanding with the discourse surrounding it. The aim of the study was to examine the inclination to pursue fashion and beauty digital entrepreneurship amongst selected final year Diploma students in a South African university. The objectives of the study were: to establish the level of interest in pursuing digital entrepreneurship with regards to fashion and beauty amongst selected final year Diploma students, to determine factors that would influence the uptake of digital entrepreneurship with reference to fashion and beauty amongst selected final year Diploma students, and to assess using the Technology Acceptance Model (TAM) the inclination to pursue digital entrepreneurship in fashion and beauty amongst selected final year Diploma students. The study employed the Technology Acceptance Model to determine whether students intended to accept digital entrepreneurship. Methodologically, the study implemented the positivist research paradigm. The research approach chosen was quantitative. A survey research design was conducted through a questionnaire, as a data collection tool, from a census of the 29 final year students studying their Diploma in Fashion Design and 65 studying for their Diploma in Somatology. Instruments were pre-tested on 10 students, 5 in the Advanced Diploma in Fashion Design and 5 in the Advanced Diploma in Somatology at DUT. Findings showed a strong interest to pursue digital entrepreneurship in the fashion and beauty space. However, hesitations related to ‘customer satisfaction’ and ‘trust’ negatively influence the uptake of digital entrepreneurship. The findings also revealed that respondents were most likely to incorporate digital technologies in their businesses and saw the importance of administrative functions and advertising skills to have when venturing into digital entrepreneurship. The study recommends the youth to: be encouraged to consider entrepreneurship by South African universities; familiarize themselves with digital entrepreneurship and get education and knowledge in that regard; acquire the necessary skills in order to venture into digital entrepreneurship.
Bio-inspired optimisation of a new cost model for minimising labour costs in computer networking infrastructure
(2024) Nketsiah, Richard Nana; Millham, Richard Charles; Agbehadji, Israel Edem
This thesis revolves around the bio-inspired optimisation of a newly formulated cost model tailored for initial installation of a user-specified computer networking infrastructure, motivated by requirements of networking industries, with a focal point on minimising labour costs. The new cost function of this infrastructure installation incorporates essential decision variables related to labour, encompassing the daily requirements and costs of both skilled and unskilled workers, their respective hourly rates, installation hours, and the overall project duration. This deliberate emphasis on labour-centric factors aim to offer nuanced insights into the intricacies of project budgeting and resource allocation. The research critically evaluates the effectiveness of the cost function by examining various factors, such as daily fixed costs, a size and complexity factor tailored to individual scenarios, and a penalty coefficient aimed at ensuring compliance with project schedules. Significantly, the deliberate exclusion of equipment, material, maintenance and operational costs underscores the focused examination of labour-related expenditures, providing a unique contribution to the optimisation landscape within the installation of the user-specified computer networking infrastructure projects. Utilising advanced bio-inspired optimisation techniques, alongside real-world data, this study endeavours to gauge the effectiveness of the new cost model in minimising labour expenses while upholding optimal network performance. The anticipated outcomes of this study extend beyond theoretical contexts to practical implications, providing actionable insights and recommendations for network infrastructure planners. The significance of labour-centric considerations in project planning and design is underscored, providing a more encompassing perspective that aligns with the evolving landscape of modern technological infrastructures. By giving attention to labour-intensive aspects within installation of computer networking infrastructure projects, the thesis aspires to enhance budgeting accuracy and streamline resource allocation processes, thereby fostering more efficient and cost-effective project outcomes.
Evaluating the level of satisfaction in higher education students with technical support services provided using fuzzy TOPSIS decision method
(2024) Pursan, Geeta; Adeliyi, Timothy T.; Joseph, Seena
The use of information and communication technologies at higher education institutions is no longer an option, but rather a need. Information Technology support is an essential factor that entails giving end users assistance with hardware and software components. Technical support for information technology has been recognized as a crucial element linked to student satisfaction because it helps students understand, access, and use technology efficiently. IT technical support services are essential for higher education students to succeed in their studies. However, the quality of IT technical support services can vary widely from institution to institution. Student satisfaction with IT technical support services is an important measure of the quality of education that students receive. Conversely, evaluating student satisfaction is a complex task, as it involves subjective assessments of service quality. This dissertation used a framework that combines three approaches: Principal Component Analysis (PCA), Service Quality (SERVQUAL), and Fuzzy TOPSIS. The successful implementation of IT technical support is aided by identifying the essential success criteria that enable efficient and effective support for students and instructors. Hence the main aim of this study is to identify and rank the key success factors for the successful implementation of IT technical support at higher education institutes. 81 key success factors identified from 100 research papers were analyzed using principal component analysis. The findings led to the identification and ranking of 25 PCs. From these findings, the SERVQUAL dimensions that featured at the top-most rankings were selected, and that being: tangibility, reliability, assurance, empathy, and responsiveness. These factors were used in the development of the questionnaire that was sent to students which measured student perceptions of the five dimensions of service quality. The proposed approach is implemented in a higher education institution in South Africa. The questionnaires were administered to a specific target of students, only those student participants’ who had contacted the IT technical team for IT technical support via the WhatsApp service communication method formed part of the study. Once data was collected, SERVQUAL which is a well-established scale for measuring service quality was used to calculate the average score for each dimension of service quality. The dimensions of service quality where students were most and least satisfied were identified. Finally, Fuzzy TOPSIS, which is a multi-criteria decisionmaking (MCDM) method that handles uncertainty and vagueness in data was used to rank the IT technical support services based on student satisfaction. The SERVQUAL results showed that the overall satisfaction level of students with IT technical support services led to a final score of 60 percent, meaning that the support services rendered were acceptable to students. The Fuzzy TOPSIS rankings identified the sub-criteria, overall being satisfied with the support services rendered as rank number one. As can be deduced that since both the SERVQUAL and Fuzzy TOPSIS methods have nominated satisfaction level as the common factor, this research indicates that the IT technical support services rendered by the IT technical support team are adequately sufficient and that the needs of the students are met and that the services rendered are highly appreciated by the students at the Durban University of Technology. This research proves that the IT support team is compliant with the quality of IT technical support services rendered to students at the Durban University of Technology however, the IT support service can be improved by the proactiveness of the technical team. This research contributes by providing useful information highlighting factors that can be used to examine areas in educational institutions that need to receive continuous and special care to generate high student satisfaction; ensure future success and gain a competitive advantage. These factors can assist the management of HEI in determining the success or failure of an institution in terms of the technical support provided to students and student satisfaction. The results of this evaluation can be used by other HEIs to improve the quality of IT technical support services and to ensure that they are meeting the needs of students.
The management of the first-year student experience programmes at the selected higher education institution : a case of business and information management undergraduates
(2024) Mbonambi, Musa Grace; Ngibe, Musawenkosi; Msomi, M
The transition from high school to university is recognised in research and theoretical literature as a distinct period of transition. To successfully transition from high school to university, students must adjust while being supported by the people and systems around them. Numerous studies show that the transition from high school to university is disconcerting and stressful, especially for first-generation students and students from poor backgrounds who experience additional challenges as a result of disadvantaged schooling. This results in declining completion and throughput rates. The aim of this study is to examine the management and effectiveness of DUT’s firstyear student programmes that were designed to manage the transition of students from high school to university. A pragmatist philosophy was adopted together with a mixed methods approach, which was deemed appropriate to support the primary aim of the study. The study’s population consisted of Business and Information Management students, Centre for Learning and Teaching (CELT) administrators, and Tutor Mentor Advisors (TMAs). To identify and select the appropriate sample size, both probability and non-probability sampling techniques were adopted. Probability sampling was used to sample 175 first-year students, while non-probability sampling was used to sample three CELT administrators coordinating the FYSE programme and five TMAs. A self-administered questionnaire, focus groups, and individual interviews served as the data collection mechanisms. The Statistical Package for the Social Sciences (SPSS) version 26.0 was used to analyse quantitative data, while thematic analysis was used to analyse qualitative information. The empirical findings revealed that the students who participated in this study found the transition to higher education difficult, and that it inevitably posed serious challenges to their academic progression and achievements. The majority of the students who participated in this study were confident that DUT’s first-year student orientation programme assisted them to adapt to university life. However, some students indicated that the orientation programme was not beneficial or was relatively unknown to them. The study also found that there was no clear collaboration between CELT and the academic departments within the university. This finding was quite alarming since the academic departments dealt directly with first-year students. The study identified a number of support programmes offered by DUT to ensure that firstyear students transition successfully to university. However, the strategic management of these programmes is a great concern, as many first-year students still face difficulties adjusting to university life. The study also found that the TMAs used different approaches to provide assistance to first-year students. The study recommends that first-year student orientation be conducted not only at the beginning of the year, but also at the beginning of the second semester. The study also recommends that CELT and academic departments collaborate to develop structures that resonate with particular departments, rather than offering workshops that are too generic and do not address departmental curricula. In addition, the study recommends that CELT establish an applicable teaching philosophy for tutoring students.
Total quality management and competitive advantage of manufacturing organizations in the Steel industry Durban Metropolis
(2024) Mncwabe, Ncazelo; Marimuthu, Ferina; Mvunabandi, Jean Damascene
This study investigated total quality management (TQM) and the competitive advantage of manufacturing organizations in the steel industry in the Durban Metropolis. While a few studies have touched on TQM and competitive advantage in Durban, they did not examine TQM and competitive advantage in steel manufacturing organizations in the city. The literature on the context of business competitiveness, total quality principles, process management and top management and other pertinent concepts was reviewed. Purposive sampling was employed to obtain a total of 100 participants from the 23 organizations in the Durban steel industry. A quantitative research design was adopted, with a questionnaire administered to gather data. The data were analyzed using confirmatory factor analysis (CFA), which was employed to prove or disprove the hypotheses, with the results presented in tables and graphs. The findings suggest that employee training, committed management, a customer-focused approach, sound relationships with suppliers, and clear goals would enable organizations in Durban’s steel industry to improve their performance and thus gain a competitive edge. The study contributes to the body of knowledge on TQM in organizations and its impact in the steel manufacturing industry. Further research is recommended to explore the impact of the adoption of TQM among South African organizations.
Enhancing the usability of a university student support services FAQ Chatbot
(2024) Essop, Luthfiya; Singh, Alveen; Wing, Jeanette
Chatbots play a vital role in customer facing interaction. They offer real-time text or voice responses via intuitive human interaction systems and are often driven by AI technologies. Despite widespread adoption, their optimisation for university environments remains elusive. With a lens on Universities of Sub-Saharan Africa, this dissertation positions usability as essential in a chatbot’s ability to provide effective support for student support services. This dissertation identifies with the dire need for more rigorous design and development in line with the needs of a modern, inclusive university sensitive and responsive to its students’ varying degrees of multiculturalism, multilingualism, socio-economic standing and technology and digital literacy baseline skills. The topic of chatbot integration in University systems has received significant attention in recent years but few have focused on the interplay between usability factors such as, anthropomorphism, NLP, or UX. This has limited our understanding of how best to enhance chatbots, specifically in University student support services. This study aimed to identify the key design factors for an enhanced usability FAQ chatbot, tailored for University student support services. In pursuit of this aim, a usability design framework as well as a FAQ chatbot was developed and tested in a popular University in South Africa. The base functional requirements were inferred from extant literature and then fused with data collected from students and administrative members of staff. The design framework was also influenced by well-known usability principles and standards from ISO, Nielsen and Shneiderman and others. Google Dialogflow was used to develop the chatbot, architected by the design framework. Based on the DSR paradigm, the research followed a systematic approach encompassing usability design, framework development, tool evaluation, and FAQ chatbot development and testing. First-year students and administrative staff members were active participants and served as change agents during the iterative DSR process. Thematic analysis was used to carefully analyse the feedback from participants during the development stages and seed this into the ongoing design process. This iterative process of development and refinement allowed for a richer understanding of how users perceive and interact with the chatbot. During analysis of the final evaluation feedback, PLS-SEM illuminated relationships, dependencies and interactions among various usability design factors which influence the chatbot's overall usability. The major contribution is a blueprint for the design and development of an effective University student support services FAQ chatbot. Theoretical contributions include a usability design framework, iterative DSR development process and evaluation and feedback instruments using robust analysis techniques. There is a need for further research and refinement at the confluence of NLP, anthropomorphism and FAQ chatbot design frameworks.
Knowledge-based word sense disambiguation for Setswana-English machine translation
(2024) Moape, Tebatso Gorgina; Ojo, Sunday O.; Olugbara, Oludayo O.
There are several challenges that hinder the development of Setswana-to-English machine translation systems. A key obstacle is the absence of machine-readable knowledge resources. This has prompted the use of the only accessible data, which originates from the government domain. While training machine-translation systems using government-domain data can offer specialized language knowledge, such training introduces obstacles such as limited vocabulary, style variation, bias, and domain specificity. Furthermore, it is noted in the literature that the ongoing problem of polysemy in a machine-translation system reduces the overall accuracy. Polysemy is a linguistic phenomenon in which a single word or phrase has multiple senses, resulting in ambiguity. The task of resolving ambiguity in natural language processing (NLP) is known as word sense disambiguation (WSD). The concept of WSD serves as an intermediate task for enhancing text understanding in NLP applications, including machine translation, information retrieval, and text summarization. Its cardinal role is to enhance the effectiveness and efficiency of these applications by ensuring the accurate selection of the appropriate sense for polysemous words in diverse contexts. This study addresses these challenges by proposing three essential components: a diversity-aware machine-readable knowledge resource for SetswanaEnglish, or the Setswana universal knowledge core (SUKC), a WSD approach to resolving lexical ambiguity; and a corresponding machine-translation model embedded with a WSD capability. Setswana-English data was collected from the existing paper-based bilingual dictionaries to achieve this purpose. Secondly, the study employed professional translators to translate space domain concepts from English to Setswana. The collected lexicon was integrated into the universal knowledge core (UKC). The Lesk algorithm which has seen various adaptations by researchers for different languages over the years was employed to address the inherent polysemy challenges. This study used a simplified, Lesk-based algorithm to resolve polysemy for Setswana; and used the bidirectional encoder representations from transformers (BERT) model for Setswana, and cosine similarity measure to embed Setswana glosses and measure semantic similarity, thus determining the accurate sense. The study employed a rule-based method embedded with the WSD algorithm for machine translation. The translation accuracy of the machine-readable dictionary was assessed by employing the developed machine-translation model; and evaluated using the BLEU score. The proposed model was tested on a combination of sentences containing both ambiguous words and those without ambiguity; and a higher BLEU score of 34.89 was achieved.
A framework for supporting technological innovation by manufacturing small and medium enterprises in KZN
(2024) Bingwa, Luyanda Loraine; Ngibe, Musawenkosi
Unemployment is an ongoing phenomenon in every country. It is rapidly increasing, which leads to a decline in the economy and other societal problems. This is particularly evident in developing countries such as South Africa, where the unemployment rate is 32.9%. The South African government has identified small and medium-sized enterprises (SMEs) as a key aspect of its strategy to reduce unemployment rates and to realise the vision outlined in the National Development Plan 2030. SMEs are major job creators and contribute significantly to the gross domestic product (GDP) of South Africa. They account for the majority of employment opportunities in the country, especially in sectors such as agriculture, manufacturing, and services. SMEs also support economic growth through their capacity for innovation and swift market adaptation. They are ideal for generating innovative ideas due to their pioneering role in adopting new technologies and are particularly adept at identifying gaps in the market which could be addressed through innovative solutions. There are ongoing debates about the uptake of technology by SMEs in African countries, including South Africa. Some scholars argue that manufacturing SMEs in South Africa have been hesitant to adopt modern technologies, which has hindered their growth and their ability to reach full potential. However, there are counterarguments that provide a more nuanced perspective on the challenges and opportunities for technology adoption among manufacturing SMEs in KwaZulu-Natal (KZN), South Africa. One significant issue is SME owners' inability to fully grasp the complexity of information and communications technology (ICT), which has a negative impact on their decision to adopt ICT. Furthermore, government regulations and compliance requirements have been a crucial factor affecting the viability and growth of manufacturing SMEs. Without a comprehensive understanding of ICT, SMEs find it challenging to make informed decisions about their investments in this field. Critically evaluating the use of Fourth Industrial Revolution (4IR) technologies as a way of improving success rates amongst manufacturing SMEs in KZN will enable the development of a framework which can provide practical guidance for the adoption of 4IR technologies by manufacturing SMEs in KZN. The objectives of this study are supported by a pragmatic methodology, which considerably expands the area of the investigation. 384 manufacturing SMEs in KZN are the target population for this study, and approaches for identification and selection of the sample size include convenience and purposive sampling. The study utilises both primary and secondary research. Interviews and questionnaires are utilised as data collection instruments. The review of literature and relevant theories such as the technology acceptance model (TAM), the technology-organisation-environment (TOE) framework, dynamic capability theory (DCT), the theory of planned behaviour (TPB), task-technology fit, process virtualisation, and the unified theory of acceptance and use of technology (UTAUT) assist in identifying and addressing potential barriers that may arise during the technology adoption process, such as cost, skills, resistance to change, and compatibility with existing systems. The primary results of this study demonstrate that digital competencies and thorough ICT knowledge are lacking in manufacturing SMEs in KZN. In addition, ICT adoption and usage in manufacturing SMEs in KZN is significantly low, which diminishes the potential of ICT as a long-term strategy. This is evident in the investigation of several factors relating to the acceptance and use of ICT by manufacturing SMEs as a longterm tool for business success. The findings of this study also suggest that manufacturing SMEs do not have the capacity to identify and implement appropriate and adequate ICTs as a sustainable strategy to improve their business viability. Based on the key findings, the study recommends that manufacturing SMEs prioritise digital literacy, which will enhance their comprehension of the potential benefits of ICT adoption. Consultation with IT professionals is recommended as a valuable means for SME owners to obtain reliable guidance and to discuss the complexities of ICT. The government should consider creating platforms to enable SMEs to express objections to regulations, contribute to amendments, and provide insight into the impact of legislation on their business.
The provision of access to electronic information by staff in Kwazulu-Natal Department of Health libraries in the digital transformation era
(2024) Ntloko, Nonhlanhla Princess; Masenya, Tlou Maggie
This study investigated the provision of staff digital access to information in KwazuluNatal Department of Health (KZN DOH) libraries. The current state of access to information was determined, challenges encountered were identified, systems and technologies used for the provision of access to information were assessed, the level of digital skills for staff members in accessing digital information was examined, and strategies for optimising the utilization of electronic information resources were discovered. National Health Digital Strategy of South Africa (2019 – 2024) emphasis on leveraging technology for better health outcomes aligns seamlessly with the health libraries' mission to provide timely and accurate health information. The goal is to create a seamless digital environment where health professionals, students, and researchers can easily access and utilise various health information resources. Digitisation in libraries is part of technological advancements of the twenty-first century that help to manage information securely and enable efficient retrieval and timeous dissemination. This study adopted interpretivist research paradigm. Qualitative research methodology was used for this study, with phenomenology as the research design. The target population for this study were student nurses, nurses, doctors, lecturers, and librarians from the thirteen (13) hospitals and nursing campus libraries in KwaZulu Natal. Non-probability, judgement, or purposive sampling was adopted, and according to Pathak (2015), it is the best sampling method for a phenomenological study. The sampling frame for this study consisted of student doctors, doctors, student nurses, librarians, lecturers, and other health staff. The study utilised a sample of 73 available and accessible participants across ten hospitals and nursing colleges with digital library resources. Data collection from the recruited participants included interviews, focus group discussions and document analysis. Semi-structured interviews were also conducted to gather participants’ background data and information on library usage. Focus group discussions were utilised to generate discussion among the diverse participants, including student nurses, lecturers, nurses, and doctors. Phenomenological reduction was used for dimension reduction during data analysis. From the librarians' perspective, budgetary technological constraints and the need for improved digital literacy among patrons are significant concerns. In response, librarians have implemented various strategies, including enhancing digital access, providing training, and introducing innovative solutions. Conversely, patrons express satisfaction with the library staff but desire improved network reliability, access to online resources, extended library hours, and further digital literacy training. As the document analysis highlights, the legislative and strategic frameworks provide a roadmap for libraries to align their services with national health objectives and embrace digital transformation. The study recommended enhancing digital infrastructure, improving digital literacy, and adopting innovative technologies to effectively transition to digital information access. The study recommends reducing the digital gap and increasing digital awareness through investment in digital skills among patrons and providing digital tools.
Exploring quality administration management practices on customer retention and satisfaction : case study of small medium micro enterprises at eThekwini Metropolitan area
(2024) Mchunu, Nkosingiphile Trevor; Ngxongo, Nduduzo Andrias; Moyane, Smangele Pretty
Small Medium Micro Enterprises (SMMEs) play a significant role in many countries’ economic growth and development. However, customer retention and satisfaction remain critical challenges facing SMMEs. This study investigated the effectiveness of quality administration management practices in SMMEs located in the eThekwini Metropolitan Area. The objectives of the study were to examine how quality administration management practices impact customer retention and satisfaction in SMMEs located in the eThekwini Metropolitan area, to comprehend the influence of training and development of employees on quality management practices and customer retention and satisfaction in small and medium-sized enterprises (SMMEs), to recognise how internal and external factors impact quality management practices in SMMEs in the eThekwini Metropolitan area and, to determine the factors that contribute to and hinder customer retention and satisfaction in SMMEs in the eThekwini Metropolitan area and propose integrated, effective quality administration management practices to address these factors. The Quality Administration Management Theory (QAMT) was adopted as a theoretical framework in this study to assert the effective role of administration and management in achieving successful business quality management practices in SMMEs. QAMT’s focus was relevant to small and medium-sized enterprises (SMEs), which often face resource constraints and struggle to implement quality management practices effectively. Due to time constraints, the geographical scope of the study was limited to the eThekwini Metropolitan Area. Potential limitations included the possibility of response bias and a relatively small sample size. The research adopted a quantitative research approach and employed a survey questionnaire to collect data from customers of selected SMMEs. Data was analysed using descriptive statistics and inferential statistics, including t-tests, ANOVA, factor analysis, and regression analysis, through the Statistical Package for the Social Sciences (SPSS) software. The findings of the study indicated that hat continuous improvement with the quality administration management practice significantly influences the customer satisfaction and in return, it positively relates to customer retention. Secondly, the results revealed that the education levels significantly had increasing impact on customer retention perceptions. The study thereafter recommended for SMMEs in the eThekwini Metropolitan area to continue striving for improvement in quality management practices achievable by implementing strategies that cater for customers with different levels of education. Policy frameworks also need to be initiated to develop and promote customer customer retention.
Factors influencing innovative leadership in mobilising small and medium enterprises (SMEs) towards smart manufacturing in Pietermaritzburg
(2023) Maphumulo, Sydney Dumisani; Nyide, Celani John
Small and medium-sized businesses (SMEs) are regarded as effective drivers of inclusive economic growth and expansion in South Africa and globally. After noting the significance of SMEs and their contribution to the economy, it is helpful to observe how their growth and sustainability is being maintained through the adoption and utilisation of smart manufacturing techniques and innovative leadership. Consequently, this study highlighted the significance of SME adoption of innovative leadership in smart manufacturing. The main aim was to critically examine factors influencing innovative leadership in mobilising SMEs towards smart manufacturing in Pietermaritzburg, KwaZulu-Natal (KZN), South Africa. A quantitative research approach was adopted with census sampling; 102 manufacturing SMEs registered in the Msunduzi Municipality database participated. The data was analysed using the latest version of SPSS V 29.0.1. The findings of this research indicate that the use of robotic technology among manufacturing SMEs is very limited. Moreover, the adoption of internet of things (IoT) and artificial intelligence technologies is still very low. Therefore, the general finding is that the utilisation of smart manufacturing processes by SMEs in the manufacturing sector in Pietermaritzburg is still in its infancy. This study also found that there is substantial evidence supporting the presence of innovative leadership practices in SMEs operating in the manufacturing sector. This provides an opportunity for these companies to advance the smart manufacturing agenda through effective leadership. The study found a number of factors that had an impact on the use of smart manufacturing processes and innovative leadership.
The impact of debt-financing costs on the growth and development of Small-Medium Enterprises in South Africa : empirical evidence from Durban
(2024) Majola, Khethiwe Prudence; Olarewaju, Odunayo Magret; Ngiba, Brian Thulane
Since small-medium enterprises are considered the backbone of every country’s economy, it is important that they have a source of finance for their growth and development. As a result, the objectives of this study were to examine the factors that impact access to debt financing; determine the effect of market imperfections on the decision of small-medium enterprises (SMEs) to apply for debt financing; evaluate the impact of debt financing as a source of finance on the growth and development of small-medium enterprises (SMEs); and lastly, examine the influence of the economic environment on small- medium enterprises (SMEs)’ growth in South Africa: Empirical evidence from Durban. A qualitative research method and a purposive sampling technique were utilized to select the participants for this study. The researcher used the formula proposed by Yamane to calculate the sample size at a 90% confidence level, where P = 10. The sample size was calculated to be 88. For the purposes of this study, a target sample size of 200 was used. The study collected primary data through a survey questionnaire from respondents who are owners and managers in the agricultural, retail, manufacturing and construction sectors in Durban, South Africa. Three hundred (300) questionnaires were administered, and the data was analyzed using the Statistical Package for the Social Sciences (SPSS). A regression analysis and Pearson’s correlation analysis were conducted to address and achieve the objectives of the study. Some of the most notable findings were small-medium enterprises (SMEs) are shutting down even though they had been funded because of weak financial management skills in utilizing funds effectively and efficiently, using funds on unrealistic market criteria, and ending up over-indebted and unable to pay back the borrowed amount with interest. Small-medium enterprises (SMEs) are finding it difficult to obtain debt financing for their growth and development due to their limited knowledge on how to go about applying for debt financing and what requirements they must meet to obtain funding.
Predicting serious crime trends in South Africa using data analytic techniques
(2024) Falope, Olayemi Success; Thakur, Surendra Colin
This dissertation aims to investigate the application of data analytics in forecasting serious crime trends in South Africa. The escalating rates of serious crimes, including homicide, robbery, and sexual assault, present significant challenges to the country's economic growth and the safety of its citizens. Recent South African crime statistics indicate a notable increase of over 9.6% in serious crimes, rising from 444,452 incidents in December 2021 to 486,960 in December 2022. This upward trajectory underscores the urgency to predict future serious crimes preemptively, facilitating the development of proactive strategies by law enforcement agencies, policymakers, and community organizations to prevent and mitigate criminal activities. To achieve this objective, this study employs a comprehensive dataset comprising historical crime records and spatial data to analyse serious crime trends across South Africa's nine provinces from 2005 to 2020. Data pre-processing techniques are applied to clean and normalize the data, ensuring its suitability for subsequent analysis. Exploratory data analysis is conducted using Python (Anaconda) and the Flourish studio environment to identify patterns, relationships, and potentially influential factors associated with serious crimes in South Africa. Various data analytics techniques, including machine learning algorithms, time series analysis, and spatial analysis, are utilized to construct models for predicting serious crime trends. These predictive models are trained using historical crime data and relevant contextual features, facilitating the identification of patterns and correlations that could inform future crime trends. The evaluation of these predictive models involves rigorous performance metrics and validation techniques to assess their predictive power, stability, and generalizability. The results reveal an increase in serious crime across South Africa, with certain provinces emerging as hotspots for specific serious crimes, such as Gauteng with a 21% increase in sexual crimes, KwaZulu-Natal with a 23.1% increase in murders, and the Western Cape with a 38% increase in drug-related crimes. This dissertation contributes to the field of crime analysis by presenting a comprehensive approach to predicting serious crime trends in South Africa. The insights gained from this research can inform the development of proactive strategies and resource allocation by law enforcement agencies, policymakers, and community organizations to address serious crimes effectively. Furthermore, this study lays the groundwork for future research in crime prediction and prevention, highlighting the potential of data analytics techniques in tackling complex societal issues. Future research may explore advanced techniques such as ensemble learning and deep learning to enhance the accuracy and robustness of predictive models.
Blockchain-Based digital records management for auditing process : a case study of Msunduzi Local Municipality
(2023) Zuma, Khulekani P.; Rajkoomar, Mogiveny; Naicker, Nalen
The Auditor General of South Africa frequently identifies poor records management as a major issue during auditing process, resulting in absence of supporting documentation and challenges in proving allegations of misconduct in Municipalities. To address this issue, the study explores the importance of a functional digital records management system and the feasibility of using blockchain technology to enhance records transparency, authenticity, security, and privacy in Msunduzi Local Municipality. It seeks to determine whether the municipality has a digital records management system, assess the potential benefits of blockchain technology, and investigate the likelihood of improved audit results through its implementation. This research employed the Records Continuum Model as a theoretical framework. Post-positivist paradigm and qualitative research approach were used in this study. The study collected data using semi-structured interviews with 15 participants from records management and auditing staff in the municipality, and data was analyzed using thematic analysis. Results from this study indicate that Msunduzi Local Municipality needs a functional digital records management system, which may positively impact audit processes. The use of blockchain technology is perceived as a viable solution to enhance records transparency, authenticity, security, and privacy. Participants in the study believe that implementing blockchain can significantly improve audit results and mitigate issues like maladministration and negative cash flows. Recommendations include the urgent establishment of a digital records management system and a feasibility study on integrating blockchain technology to strengthen records management and auditing practices in the municipality. These measures are seen as essential for promoting transparency and accountability in the public sector.
Cost control and operational performance at eThekwini human settlement unit
(2024) Jwara, Msawenkosi Cedric; Marimuthu, Ferina; Nzuza, Zwelihle Wiseman
Government municipalities have encountered notable challenges due to their inability to undergo necessary revitalisation and keep pace with the ongoing modernisation of the contemporary business environment. Within South African municipalities, prevailing cost accounting methods exhibit a lack of alignment with contemporary advancements in cost management. This misalignment has resulted in delays in delivering vital community services due to an insufficient grasp of strategic resource cost control. Consequently, there arises a distinct imperative to undertake an inquiry into the practical cost control techniques employed, the determinative factors influencing cost controls and the resultant effectiveness of such controls in improving operational performance of the eThekwini Human Settlement Unit (HSU). This study aims to identify the role of cost controls in improving the eThekwini HSU's operational performance. Specific objectives include investigating cost control techniques, factors influencing cost controls, and their effectiveness. Employing a quantitative approach, this study used a questionnaire instrument with closed-ended questions to gather data from 44 purposefully selected HSU employees. Data analysis involved descriptive and inferential statistics using SPSS (version 27®), ensuring reliability and validity. Ethical considerations were observed throughout the research process. The investigation revealed that the eThekwini HSU implements an array of strategies encompassing budgetary frameworks, audit techniques, cost estimation and cost analysis tools in its pursuit of cost containment. Conversely, negative factors of cost control were identified, prominently including challenges rooted in deficient communication, a lack of administrative accountability and socio-political influences. In the context of perceptions, a notable agreement emerged amongst the surveyed personnel, showing the effectiveness of established cost control mechanisms. Based on the findings derived from this study, it is recommended that the eThekwini HSU take proactive measures to address the identified challenges in cost control. This research contributes to the adoption of modern cost control techniques in the eThekwini HSU and, by extension, other municipalities. Improved cost management processes enhance service delivery and benefit citizens by ensuring efficient resource allocation.

Browse

Recent Submissions