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Consumer artificial intelligence impact on organisations' data analytics and business intelligence processes
Dissertation (Mcom (Informatics))--University of Pretoria, 2024.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2025
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| _version_ | 1867613498988560385 |
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| access_status_str | Open Access |
| author2 | Hattingh, Maria J. (Marie) |
| author_browse | Hattingh, Maria J. (Marie) |
| author_facet | Hattingh, Maria J. (Marie) |
| collection | Thesis |
| dc_rights_str_mv | © 2023 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (Mcom (Informatics))--University of Pretoria, 2024. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/100855 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:37:06.816Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/100855 Consumer artificial intelligence impact on organisations' data analytics and business intelligence processes Hattingh, Maria J. (Marie) u19119853@tuks.co.za Bownass, Britney UCTD Sustainable Development Goals (SDGs) Consumer artificial intelligence (CAI) Data analysis Business intelligence (BI) Business processes Organisational improvement Dissertation (Mcom (Informatics))--University of Pretoria, 2024. This study examines people's intention to use consumer AI in data analysis and business intelligence. Introducing consumer AI in data analysis can make individuals more productive and efficient. However, some individuals fear how this innovation will change their employability. This study aims to determine which data analysis and business intelligence tasks consumer AI is best suited for, which characteristics of consumer AI make it appropriate for use in these tasks, and the factors organisations need to consider when introducing consumer AI in their data analysis and business intelligence processes. In meeting these research objectives, the study ultimately aims to determine to what extent consumer AI can be used in data analysis and business intelligence. This study adopted an interpretivist philosophy to understand the nuances. Qualitative data was collected through semi-structured interviews with fifteen analysts. A theoretical foundation was constructed by integrating three prominent theories, namely, the Unified Theory of Acceptance and Use of Technology (UTAUT), the Innovation Resistance Theory (IRT), and the Technology Organisation and Environment (TOE) framework. This theoretical foundation was used to develop the interview guide. The results showed that most participants believe consumer AI is best suited for data analysis. However, multiple participants indicated that consumer AI is useful in pre-processing data and visualising findings, ultimately increasing business intelligence and leading to better-informed organisational decisions. The participants identified eight characteristics that make consumer AI appropriate for data analysis and business intelligence. One of the main characteristics is that the chatbot is easy to use and that users can communicate with the application in natural language. The data revealed seven consumer AI drivers and six barriers ultimately impacting an organisation's adoption of consumer AI in data analysis and business intelligence. Informatics Mcom (Informatics) Unrestricted Engineering, Built Environment and Information Technology None 2025-02-13T13:58:21Z 2025-02-13T13:58:21Z 2024-04 2024-09 Dissertation * A2025 http://hdl.handle.net/2263/100855 https://doi.org/10.25403/UPresearchdata.28269692 en © 2023 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | UCTD Sustainable Development Goals (SDGs) Consumer artificial intelligence (CAI) Data analysis Business intelligence (BI) Business processes Organisational improvement Consumer artificial intelligence impact on organisations' data analytics and business intelligence processes |
| title | Consumer artificial intelligence impact on organisations' data analytics and business intelligence processes |
| title_full | Consumer artificial intelligence impact on organisations' data analytics and business intelligence processes |
| title_fullStr | Consumer artificial intelligence impact on organisations' data analytics and business intelligence processes |
| title_full_unstemmed | Consumer artificial intelligence impact on organisations' data analytics and business intelligence processes |
| title_short | Consumer artificial intelligence impact on organisations' data analytics and business intelligence processes |
| title_sort | consumer artificial intelligence impact on organisations data analytics and business intelligence processes |
| topic | UCTD Sustainable Development Goals (SDGs) Consumer artificial intelligence (CAI) Data analysis Business intelligence (BI) Business processes Organisational improvement |
| url | http://hdl.handle.net/2263/100855 https://doi.org/10.25403/UPresearchdata.28269692 |