Exploring Employees’ Accountability in Knowledge Management Systems Enhanced by Generative Artificial Intelligence
Research objectives and hypothesis/research questions The study aims to understand managerial attitudes toward accountability when using GenAI-driven data for decision-making and to identify procedures or regulations that could minimize erroneous data usage. Research methods Employing a qualitative...
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| Format: | Article |
| Language: | English |
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Military University of Technology, Warsaw
2024-12-01
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| Series: | Nowoczesne Systemy Zarządzania |
| Subjects: | |
| Online Access: | https://nsz.wat.edu.pl/Exploring-Employees-Accountability-in-Knowledge-Management-Systems-Enhanced-by-Generative,203481,0,2.html |
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| _version_ | 1850148529695096832 |
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| author | Robert Strelau |
| author_facet | Robert Strelau |
| author_sort | Robert Strelau |
| collection | DOAJ |
| description | Research objectives and hypothesis/research questions
The study aims to understand managerial attitudes toward accountability when using GenAI-driven data for decision-making and to identify procedures or regulations that could minimize erroneous data usage.
Research methods
Employing a qualitative approach, the study collected insights from senior managers through interviews. Participants shared perspectives on employee responsibility for GenAI-informed decisions and suggested methods to ensure data accuracy. The analysis of these insights facilitated the development of a potential framework for GenAI adoption in KM.
Main results
Findings reveal that most managers view employees as ultimately accountable for decisions, although they acknowledge GenAI as a supportive rather than a substitutive tool. The need for clear guidelines, thorough testing phases, and the implementation of verification procedures emerged as key strategies for minimizing the risks of inaccurate or false data. Managers also highlighted the importance of well-defined roles, with explicit boundaries for GenAI usage.
Implications for theory and practice
The study contributes to theoretical discourse by pinpointing potential accountability structures in GenAI-driven decision-making and by proposing a framework that addresses data verification challenges. Practically, it offers organizations a structured approach to integrating GenAI into KM, emphasizing the need for precise regulations, testing protocols, and ongoing oversight. These insights encourage further exploration of the ethical and social dimensions of GenAI in business settings. |
| format | Article |
| id | doaj-art-e4a6a18547484a9eba1afe798d1d4eb7 |
| institution | OA Journals |
| issn | 1896-9380 2719-860X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Military University of Technology, Warsaw |
| record_format | Article |
| series | Nowoczesne Systemy Zarządzania |
| spelling | doaj-art-e4a6a18547484a9eba1afe798d1d4eb72025-08-20T02:27:14ZengMilitary University of Technology, WarsawNowoczesne Systemy Zarządzania1896-93802719-860X2024-12-01194799410.37055/nsz/203481203481Exploring Employees’ Accountability in Knowledge Management Systems Enhanced by Generative Artificial IntelligenceRobert Strelau0https://orcid.org/0000-0001-8815-3447Warsaw School of Economics, PolandResearch objectives and hypothesis/research questions The study aims to understand managerial attitudes toward accountability when using GenAI-driven data for decision-making and to identify procedures or regulations that could minimize erroneous data usage. Research methods Employing a qualitative approach, the study collected insights from senior managers through interviews. Participants shared perspectives on employee responsibility for GenAI-informed decisions and suggested methods to ensure data accuracy. The analysis of these insights facilitated the development of a potential framework for GenAI adoption in KM. Main results Findings reveal that most managers view employees as ultimately accountable for decisions, although they acknowledge GenAI as a supportive rather than a substitutive tool. The need for clear guidelines, thorough testing phases, and the implementation of verification procedures emerged as key strategies for minimizing the risks of inaccurate or false data. Managers also highlighted the importance of well-defined roles, with explicit boundaries for GenAI usage. Implications for theory and practice The study contributes to theoretical discourse by pinpointing potential accountability structures in GenAI-driven decision-making and by proposing a framework that addresses data verification challenges. Practically, it offers organizations a structured approach to integrating GenAI into KM, emphasizing the need for precise regulations, testing protocols, and ongoing oversight. These insights encourage further exploration of the ethical and social dimensions of GenAI in business settings.https://nsz.wat.edu.pl/Exploring-Employees-Accountability-in-Knowledge-Management-Systems-Enhanced-by-Generative,203481,0,2.htmlaccountabilitydecision-makinggenerative aiknowledge management systemslarge language models |
| spellingShingle | Robert Strelau Exploring Employees’ Accountability in Knowledge Management Systems Enhanced by Generative Artificial Intelligence Nowoczesne Systemy Zarządzania accountability decision-making generative ai knowledge management systems large language models |
| title | Exploring Employees’ Accountability in Knowledge Management Systems Enhanced by Generative Artificial Intelligence |
| title_full | Exploring Employees’ Accountability in Knowledge Management Systems Enhanced by Generative Artificial Intelligence |
| title_fullStr | Exploring Employees’ Accountability in Knowledge Management Systems Enhanced by Generative Artificial Intelligence |
| title_full_unstemmed | Exploring Employees’ Accountability in Knowledge Management Systems Enhanced by Generative Artificial Intelligence |
| title_short | Exploring Employees’ Accountability in Knowledge Management Systems Enhanced by Generative Artificial Intelligence |
| title_sort | exploring employees accountability in knowledge management systems enhanced by generative artificial intelligence |
| topic | accountability decision-making generative ai knowledge management systems large language models |
| url | https://nsz.wat.edu.pl/Exploring-Employees-Accountability-in-Knowledge-Management-Systems-Enhanced-by-Generative,203481,0,2.html |
| work_keys_str_mv | AT robertstrelau exploringemployeesaccountabilityinknowledgemanagementsystemsenhancedbygenerativeartificialintelligence |