Usage of AI in Sustainable Knowledge Management and Innovation Processes; Data Analytics in the Electricity Sector
Successful organisations prioritise product quality and customer satisfaction. Non-financial indicators are crucial for measuring performance, requiring specific financial and technology management knowledge. Effective knowledge management and entrepreneurial activity significantly impact performanc...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-11-01
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| Series: | FinTech |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2674-1032/2/4/40 |
| _version_ | 1827574870267920384 |
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| author | Lea Kocjancic Sergej Gricar |
| author_facet | Lea Kocjancic Sergej Gricar |
| author_sort | Lea Kocjancic |
| collection | DOAJ |
| description | Successful organisations prioritise product quality and customer satisfaction. Non-financial indicators are crucial for measuring performance, requiring specific financial and technology management knowledge. Effective knowledge management and entrepreneurial activity significantly impact performance, vital to the country’s economic factors. Electricity is crucial to society’s development. Renewable energy sources such as solar, wind, hydropower, and biomass can generate sustainable electricity. Managing environmental, social, and economic aspects is essential for sustainable societal and virtual development. In this study, the central element of novelty is associated with the dependent variable Nominal Labour Productivity per Employee. This research shows that effective knowledge management impacts a company’s business performance. Based on secondary data from various sources, we have used factor analysis to assess the interrelationship between the factors and econometric dimensionalities. Accompanied by this econometric approach, the research methodology aims to present hybrid models based on econometric techniques and artificial intelligence (AI) networks. Based on the principal component method analysis results, we show the interdependence of 30 variables in the micro and macro environment. The new components of the correlated variables show how knowledge and innovation are related to the economic performance of society, and nominal employee productivity is a valuable indicator for measuring economic efficiency. Nevertheless, AI, a knowledge management product, provides helpful comments on the econometric results. |
| first_indexed | 2024-03-08T20:47:13Z |
| format | Article |
| id | doaj.art-d923789147ec4d8b9c1f7123acf72c3d |
| institution | Directory Open Access Journal |
| issn | 2674-1032 |
| language | English |
| last_indexed | 2024-03-08T20:47:13Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | FinTech |
| spelling | doaj.art-d923789147ec4d8b9c1f7123acf72c3d2023-12-22T14:08:01ZengMDPI AGFinTech2674-10322023-11-012471873610.3390/fintech2040040Usage of AI in Sustainable Knowledge Management and Innovation Processes; Data Analytics in the Electricity SectorLea Kocjancic0Sergej Gricar1Faculty of Economics and Informatics, University of Novo Mesto, Na Loko 2, 8000 Novo Mesto, SloveniaFaculty of Business and Management Sciences, University of Novo Mesto, Na Loko 2, 8000 Novo Mesto, SloveniaSuccessful organisations prioritise product quality and customer satisfaction. Non-financial indicators are crucial for measuring performance, requiring specific financial and technology management knowledge. Effective knowledge management and entrepreneurial activity significantly impact performance, vital to the country’s economic factors. Electricity is crucial to society’s development. Renewable energy sources such as solar, wind, hydropower, and biomass can generate sustainable electricity. Managing environmental, social, and economic aspects is essential for sustainable societal and virtual development. In this study, the central element of novelty is associated with the dependent variable Nominal Labour Productivity per Employee. This research shows that effective knowledge management impacts a company’s business performance. Based on secondary data from various sources, we have used factor analysis to assess the interrelationship between the factors and econometric dimensionalities. Accompanied by this econometric approach, the research methodology aims to present hybrid models based on econometric techniques and artificial intelligence (AI) networks. Based on the principal component method analysis results, we show the interdependence of 30 variables in the micro and macro environment. The new components of the correlated variables show how knowledge and innovation are related to the economic performance of society, and nominal employee productivity is a valuable indicator for measuring economic efficiency. Nevertheless, AI, a knowledge management product, provides helpful comments on the econometric results.https://www.mdpi.com/2674-1032/2/4/40artificial intelligenceelectricityinnovationknowledgesustainability |
| spellingShingle | Lea Kocjancic Sergej Gricar Usage of AI in Sustainable Knowledge Management and Innovation Processes; Data Analytics in the Electricity Sector FinTech artificial intelligence electricity innovation knowledge sustainability |
| title | Usage of AI in Sustainable Knowledge Management and Innovation Processes; Data Analytics in the Electricity Sector |
| title_full | Usage of AI in Sustainable Knowledge Management and Innovation Processes; Data Analytics in the Electricity Sector |
| title_fullStr | Usage of AI in Sustainable Knowledge Management and Innovation Processes; Data Analytics in the Electricity Sector |
| title_full_unstemmed | Usage of AI in Sustainable Knowledge Management and Innovation Processes; Data Analytics in the Electricity Sector |
| title_short | Usage of AI in Sustainable Knowledge Management and Innovation Processes; Data Analytics in the Electricity Sector |
| title_sort | usage of ai in sustainable knowledge management and innovation processes data analytics in the electricity sector |
| topic | artificial intelligence electricity innovation knowledge sustainability |
| url | https://www.mdpi.com/2674-1032/2/4/40 |
| work_keys_str_mv | AT leakocjancic usageofaiinsustainableknowledgemanagementandinnovationprocessesdataanalyticsintheelectricitysector AT sergejgricar usageofaiinsustainableknowledgemanagementandinnovationprocessesdataanalyticsintheelectricitysector |