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Leveraging business Intelligence and analytics to improve decision-making and organisational success
In a complex and dynamic organisational environment, challenges and dilemmas exist on how to maximise the value of Business Intelligence and Analytics (BI&A). The expectation of BI&A is to improve decision-making for core business processes that drive business performance. A multi-disciplinary revie...
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| Format: | Thesis |
| Language: | English |
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Department of Information Systems
2018
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| _version_ | 1867614420728807424 |
|---|---|
| access_status_str | Open Access |
| author | Mushore, Rutendo |
| author2 | Kyobe, Michael |
| author_browse | Kyobe, Michael Mushore, Rutendo |
| author_facet | Kyobe, Michael Mushore, Rutendo |
| author_sort | Mushore, Rutendo |
| collection | Thesis |
| description | In a complex and dynamic organisational environment, challenges and dilemmas exist on how to maximise the value of Business Intelligence and Analytics (BI&A). The expectation of BI&A is to improve decision-making for core business processes that drive business performance. A multi-disciplinary review of theories from the domains of strategic management, technology adoption and economics claims that tasks, technology, people and structures (TTPS) need to be aligned for BI&A to add value to decision-making. However, these imperatives interplay, making it difficult to determine how they are configured. Whilst the links between TTPS have been previously recognised in the Socio-Technical Systems theory, no studies have delved into the issue of their configuration. This configuration is addressed in this study by adopting the fit as Gestalts approach, which examines the relationships among these elements and also determines how best to align them. A Gestalt looks at configurations that arise based on the level of coherence and helps determine the level of alignment amongst complex relationships. This study builds on an online quantitative survey tool based on a conceptual model for aligning TTPS. The alignment model contributes to the conceptual development of alignment of TTPS. Data was collected from organisations in a South African context. Individuals who participated in the survey came from the retail, insurance, banking, telecommunications and manufacturing industry sectors. This study's results show that there is close alignment that emerges between TTPS in Cluster 6 which comprises of IT experts and financial planners. Adequate training, coupled with structures encouraging usage of Business Intelligence and Analytics (BI&A), result in higher organisational success. This is because BI&A technology is in sync with the tasks it is being used for and users have high self-efficacy. Further analysis shows that poor organisational performance can be linked to gaps in alignment and the lack of an organisational culture that motivates usage of BI&A tools. This is because there is misalignment; therefore respondents do not find any value in using BI&A, thus impacting organisational performance. Applying a configurational approach helps researchers and practitioners identify coherent patterns that work well cohesively and comprehensively. The tangible contribution of this study is the conceptual model presented to achieve alignment. In essence, organisations can use the model for aligning tasks, technology, people and structures to better identify ideal configurations of the factors which are working cohesively and consequently find ways of leveraging Business intelligence and Analytics. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/27408 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:51:45.999Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2018 |
| publishDateRange | 2018 |
| publishDateSort | 2018 |
| publisher | Department of Information Systems |
| publisherStr | Department of Information Systems |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/27408 Leveraging business Intelligence and analytics to improve decision-making and organisational success Mushore, Rutendo Kyobe, Michael Information Systems Business intelligence Data analytics Decision-making In a complex and dynamic organisational environment, challenges and dilemmas exist on how to maximise the value of Business Intelligence and Analytics (BI&A). The expectation of BI&A is to improve decision-making for core business processes that drive business performance. A multi-disciplinary review of theories from the domains of strategic management, technology adoption and economics claims that tasks, technology, people and structures (TTPS) need to be aligned for BI&A to add value to decision-making. However, these imperatives interplay, making it difficult to determine how they are configured. Whilst the links between TTPS have been previously recognised in the Socio-Technical Systems theory, no studies have delved into the issue of their configuration. This configuration is addressed in this study by adopting the fit as Gestalts approach, which examines the relationships among these elements and also determines how best to align them. A Gestalt looks at configurations that arise based on the level of coherence and helps determine the level of alignment amongst complex relationships. This study builds on an online quantitative survey tool based on a conceptual model for aligning TTPS. The alignment model contributes to the conceptual development of alignment of TTPS. Data was collected from organisations in a South African context. Individuals who participated in the survey came from the retail, insurance, banking, telecommunications and manufacturing industry sectors. This study's results show that there is close alignment that emerges between TTPS in Cluster 6 which comprises of IT experts and financial planners. Adequate training, coupled with structures encouraging usage of Business Intelligence and Analytics (BI&A), result in higher organisational success. This is because BI&A technology is in sync with the tasks it is being used for and users have high self-efficacy. Further analysis shows that poor organisational performance can be linked to gaps in alignment and the lack of an organisational culture that motivates usage of BI&A tools. This is because there is misalignment; therefore respondents do not find any value in using BI&A, thus impacting organisational performance. Applying a configurational approach helps researchers and practitioners identify coherent patterns that work well cohesively and comprehensively. The tangible contribution of this study is the conceptual model presented to achieve alignment. In essence, organisations can use the model for aligning tasks, technology, people and structures to better identify ideal configurations of the factors which are working cohesively and consequently find ways of leveraging Business intelligence and Analytics. 2018-02-07T12:14:26Z 2018-02-07T12:14:26Z 2017 Master Thesis Masters MCom http://hdl.handle.net/11427/27408 eng application/pdf Department of Information Systems Faculty of Commerce University of Cape Town |
| spellingShingle | Information Systems Business intelligence Data analytics Decision-making Mushore, Rutendo Leveraging business Intelligence and analytics to improve decision-making and organisational success |
| thesis_degree_str | Master's |
| title | Leveraging business Intelligence and analytics to improve decision-making and organisational success |
| title_full | Leveraging business Intelligence and analytics to improve decision-making and organisational success |
| title_fullStr | Leveraging business Intelligence and analytics to improve decision-making and organisational success |
| title_full_unstemmed | Leveraging business Intelligence and analytics to improve decision-making and organisational success |
| title_short | Leveraging business Intelligence and analytics to improve decision-making and organisational success |
| title_sort | leveraging business intelligence and analytics to improve decision making and organisational success |
| topic | Information Systems Business intelligence Data analytics Decision-making |
| url | http://hdl.handle.net/11427/27408 |
| work_keys_str_mv | AT mushorerutendo leveragingbusinessintelligenceandanalyticstoimprovedecisionmakingandorganisationalsuccess |