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Towards a human-machine integrated model for organisational knowledge creation - a case for knowledge-enriched machine learning
Thesis (MPhil)--Stellenbosch University, 2025.
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| Format: | Thesis |
| Language: | en_ZA |
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Stellenbosch : Stellenbosch University
2025
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| _version_ | 1867613905440735232 |
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| access_status_str | Open Access |
| author | du Plessis, Susanna Johanna |
| author2 | Britz, Katarina |
| author_browse | Britz, Katarina du Plessis, Susanna Johanna |
| author_facet | Britz, Katarina du Plessis, Susanna Johanna |
| author_sort | du Plessis, Susanna Johanna |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description |
Thesis (MPhil)--Stellenbosch University, 2025. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/132168 |
| institution | Stellenbosch University (South Africa) |
| language | en_ZA |
| last_indexed | 2026-06-10T12:43:34.445Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/132168 Towards a human-machine integrated model for organisational knowledge creation - a case for knowledge-enriched machine learning du Plessis, Susanna Johanna Britz, Katarina Stellenbosch University. Faculty of Arts and Social Sciences. Dept. of Information Science. Knowledge management Information technology -- Management Organizational effectiveness Organizational learning UCTD Thesis (MPhil)--Stellenbosch University, 2025. du Plessis, S. J. 2025. Towards a Human-Machine Integrated Model for Organisational Knowledge Creation - A Case for Knowledge-Enriched Machine Learning. Unpublished masters thesis. Stellenbosch: Stellenbosch University [online]. Available: https://scholar.sun.ac.za/items/6642af35-cffc-4456-a3e5-541e5ec7a2f6 ENGLISH SUMMARY: It is widely agreed that knowledge is key to organisational success. Organisations continuously need to create new knowledge to keep control of their dynamic business environment, to timeously respond to change, to improve and innovate and to ultimately achieve business success and survive. This requires intentional initiatives from organisations to create and develop new knowledge that can be embodied in their operations, systems, products and services. This study aims at guiding and navigating enhanced organisational knowledge creation in dynamic business environments, by taking advantage of the best of both human- and machine contributions, in an era of rapid technological advancements and AI. The seminal SECI model of Nonaka and his colleagues captures their theory, that the continuous conversion between tacit and explicit knowledge, creates new organisational knowledge. While the SECI model has been revised several times over the years, it remains primarily human-focused. From a data-driven technology perspective, we have seen significant advancements since the 1990s. Artificial Intelligence (AI) and in particular Machine Learning (ML) offers great potential for organisations to support their learning and to discover, uncover and create new knowledge. Research on integrating external knowledge into ML pipelines, referred to as Knowledge-Enriched Machine Learning (KEML), aims to further improve ML by addressing challenges such as data limitations, interpretability, explainability, performance and knowledge conformity. With KEML representing machines, this study investigates the integration of human- and machine contributions towards enhanced organisational knowledge creation. Humans and machines both have unique abilities and strengths and have much to offer in a partnership that capitalises on the best of both. This leads to the design, development and proposal of the SECI-Machine Partnership (SECI-MaP) model in this study. The SECI-MaP model extends Nonaka's SECI model and captures a human-machine symbiotic and synergistic partnership for enhanced organisational knowledge creation. The model consists of three layers. It keeps the human SECI base at its core, while the outer layer presents machine contributions. The human-integration layer (middle layer) mediates human and machine contributions. The conceptual SECIMaP model explains and enhances our understanding of the potential offered by such a partnership. It illustrates how sufficiently mediated and applied combined efforts of humans and machines could be greater than the sum of their individual contributions. AFRIKAANSE OPSOMMING: Dit word algemeen aanvaar dat kennis kern is tot die sukses van 'n organisasie. Organisasies moet voortdurend nuwe kennis skep om te 'n dinamiese besigheidsomgewing hanteer, betyds op verandering te kan reageer, te verbeter en te innoveer en, uiteindelik, om suksesvol te wees en te oorleef. Dit vereis doelgerigte inisiatiewe om nuwe kennis te skep en ontwikkel, en om dit sodoende deel te kan maak van prosesse, stelsels, produkte en dienste. Die invloedryke SECI model van Nonaka en sy kollegas verteenwoordig hulle teorie dat die deurlopende omskakeling tussen onuitspreekbare en eksplisiete kennis, nuwe organisatoriese kennis skep. Die invloedryke SECI model het sedert die 1990s paradigmatiese status behaal, wat ondersteun is deur die sukses van Japanese firmas wat hierdie kennis-skeppende beginsels begin toepas het. Alhoewel die SECI model oor die jare heelwat hersien is, bly dit 'n model wat hoofsaaklik op menslike bydrae fokus. Daar was sedert die 1990s noemenswaardige vooruitgang ten opsige van data-gedrewe tegnologie. Kunsmatige intelligensie, en meer spesifiek masjienleer, bied groot potensiaal om leer binne organisasies te ondersteun en om nuwe kennis te ontdek, te ontgun en te skep. Kennisverrykde masjienleer (KEML) verwys na die navorsingveld waar eksterne kennis in masjienleerstelsels geintegreer word. Die doelwit daarvan is om masjienleer verder te verbeter deur uitdagings soos beperkings in terme van data, interpreteerbaarheid, verduidelikbaarheid, akkuraatheid en betroubaarheid aan te spreek. In die studie word die integrasie van die bydrae van mense en masjiene ondersoek, met die oog op verbeterde organisatoriese kennis-skepping, waar KEML die rol van masjiene verteenwoordig. Mense en masjiene het beide unieke vermoens en sterkpunte. Beide kan baie bied as deel van 'n vennootskap wat albei optimaal benut. Dit inspireer en lei tot die ontwerp, onwikkeling en voorstel van die SECI-model uit, en verteenwoordig ‘n mense Masjien vennootskap (SECI-MaP) model deur hierdie studie. Die SECI-MaP model brei Nonaka se SECI model uit, -masjien simbiotiese en sinergistiese vennootskap met verbeterde organisatories kennis-skepping ten doel. Die konseptuele SECI-MaP model verduidelik en help ons om die potensiaal van so ‘n vennootskap beter te verstaan. Die langer-termyn doelwit van die studie is dus om, te midde van vinnige tegnologiese vooruitgang, verbeterde organisatorieses kennis-skepping in dinamiese besigheidsomstandighede te navigeer en te bevorder. Masters 2025-05-28T11:25:55Z 2025-05-28T11:25:55Z 2025-03 Thesis https://scholar.sun.ac.za/handle/10019.1/132168 en_ZA Stellenbosch University xi, 109 pages : illustrations application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Knowledge management Information technology -- Management Organizational effectiveness Organizational learning UCTD du Plessis, Susanna Johanna Towards a human-machine integrated model for organisational knowledge creation - a case for knowledge-enriched machine learning |
| title | Towards a human-machine integrated model for organisational knowledge creation - a case for knowledge-enriched machine learning |
| title_full | Towards a human-machine integrated model for organisational knowledge creation - a case for knowledge-enriched machine learning |
| title_fullStr | Towards a human-machine integrated model for organisational knowledge creation - a case for knowledge-enriched machine learning |
| title_full_unstemmed | Towards a human-machine integrated model for organisational knowledge creation - a case for knowledge-enriched machine learning |
| title_short | Towards a human-machine integrated model for organisational knowledge creation - a case for knowledge-enriched machine learning |
| title_sort | towards a human machine integrated model for organisational knowledge creation a case for knowledge enriched machine learning |
| topic | Knowledge management Information technology -- Management Organizational effectiveness Organizational learning UCTD |
| url | https://scholar.sun.ac.za/handle/10019.1/132168 |
| work_keys_str_mv | AT duplessissusannajohanna towardsahumanmachineintegratedmodelfororganisationalknowledgecreationacaseforknowledgeenrichedmachinelearning |