Please use this identifier to cite or link to this item:
https://etd.cput.ac.za/handle/20.500.11838/3950
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | De la Harpe, André Charles | en_US |
dc.contributor.author | Swaartbooi, Luyanda Lincoln | en_US |
dc.date.accessioned | 2024-01-23T13:01:46Z | - |
dc.date.available | 2024-01-23T13:01:46Z | - |
dc.date.issued | 2022 | - |
dc.identifier.uri | https://etd.cput.ac.za/handle/20.500.11838/3950 | - |
dc.description | Thesis (MTech (Business Information Systems))--Cape Peninsula University of Technology, 2022 | en_US |
dc.description.abstract | Digitalisation, the internet and mobile data are offering large amounts of unstructured data that can be analysed and utilised by Financial Service Providers (FSPs) to achieve better effectiveness and competitive advantages. Big data analytics (BDA) is a tool for supporting the competitive advantages of FSPs by increasing data driven strategies. With the new stream of technologies, FSPs are facing challenges on how to utilise their existing data in creating new innovative products or enhancing the existing services so that their customers can benefit. Despite the high adoption rate of digital strategies by South African FSPs, little is known on how these FSPs can use Big Data (BD) to differentiate their customer segments and to create a competitive advantage. The following research questions are asked: (i) What are the challenges FSPs face when using BD analytics in building and differentiation customer segmentation to create a competitive advantage and (ii) How can FSPs utilise BD analytics in order to create a competitive advantage? The aim of this study is to explore how FSPs can use BD to differentiate their customer segments and to create a competitive advantage. The objective of the study is to (i) determine if FSPs sees the value of BD for their businesses, (ii) identify any challenges faced by FSPs to include BD strategy to their businesses, (iii) identify FSPs strategy to meet their customer’s needs and (iv) examine the way FSPs are measuring their success. An inductive research approach are followed on this study. Qualitative survey (experts) using interviews by means of semi-structured questionnaires were used during data collection. Fifteen FSPs were used to obtain experts (15) within the SMMEs. The sampling was done by non-randomly, purposively and convenient selected techniques. Data was collected by recording the interviews. Recordings were transcribed and the analysed. A thematic analysis was done on the data. The study considered several ethical issues and the researcher made sure that all research ethical standards were adhere to. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Cape Peninsula University of Technology | en_US |
dc.subject | Financial services industry -- Data processing | en_US |
dc.subject | Big data | en_US |
dc.subject | Data mining | en_US |
dc.subject | Business planning -- Data processing | en_US |
dc.subject | Small business -- Management | en_US |
dc.subject | Small business -- Data processing | en_US |
dc.title | The role of big data for the development of opportunities for SMMEs | en_US |
dc.type | Thesis | en_US |
Appears in Collections: | Financial Information Systems - Masters Degrees |
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Swaartbooi_Luyanda_203082850.pdf | 2.08 MB | Adobe PDF | View/Open |
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