Exploring the impact of AI on employee self-competence performance key variables and outcomes
Abstract In the dynamic business world, organizations must upgrade their functioning and develop human resources skills to achieve optimal performance. Researchers are currently concentrating on strategic human resources management practices that utilize AI technology, with the aim of enhancing empl...
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| Format: | Article |
| Language: | English |
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Springer
2025-07-01
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| Series: | Discover Sustainability |
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| Online Access: | https://doi.org/10.1007/s43621-025-01438-9 |
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| author | Anas Amayreh Mohammad A. Ta’Amnha Ihab K. Magableh Maher H. Mahrouq Salsabila Aisyah Alfaiza |
| author_facet | Anas Amayreh Mohammad A. Ta’Amnha Ihab K. Magableh Maher H. Mahrouq Salsabila Aisyah Alfaiza |
| author_sort | Anas Amayreh |
| collection | DOAJ |
| description | Abstract In the dynamic business world, organizations must upgrade their functioning and develop human resources skills to achieve optimal performance. Researchers are currently concentrating on strategic human resources management practices that utilize AI technology, with the aim of enhancing employee competency and achieving the highest possible organizational goals. Therefore, the purpose of this study is to investigate the potential impact of AI experience, AI utilization, AI technical ability, and AI app usefulness on employee self-competence performance in Jordanian pharmaceutical manufacturers. The variables under investigation included AI experience (X1), AI utilization (X2), AI technical ability (X3), AI app usefulness (X4), and individual competency performance (Y). This study employs a purposive sampling technique to gather quantitative data. The SPSS software application analyzes the primary data collected through a questionnaire in this study. The findings show that the AI experience, AI utilization, AI technical ability, and AI app usefulness have a significant positive effect on employee self-competence performance. The findings of this study have implications for stakeholders seeking to enhance individual manufacturing performance. This research identifies the effectiveness of AI experience, use, technical proficiency, and application utility as crucial factors influencing employee performance. These findings provide vital insights for business leaders seeking to improve productivity and operational efficiency. Furthermore, these findings provide a robust basis for evaluating performance and enhancing decision-making processes. |
| format | Article |
| id | doaj-art-c3f6e2b8d3a54d6984b8351e564faaea |
| institution | Kabale University |
| issn | 2662-9984 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Sustainability |
| spelling | doaj-art-c3f6e2b8d3a54d6984b8351e564faaea2025-08-20T03:37:19ZengSpringerDiscover Sustainability2662-99842025-07-016112310.1007/s43621-025-01438-9Exploring the impact of AI on employee self-competence performance key variables and outcomesAnas Amayreh0Mohammad A. Ta’Amnha1Ihab K. Magableh2Maher H. Mahrouq3Salsabila Aisyah Alfaiza4Department of Business Technology, Business School, Al-Ahliyya Amman UniversityCollege of Business Administration, American University of the Middle EastArab Planning InstituteAssociation of Banks in JordanSchool of Economics and Business, Telkom UniversityAbstract In the dynamic business world, organizations must upgrade their functioning and develop human resources skills to achieve optimal performance. Researchers are currently concentrating on strategic human resources management practices that utilize AI technology, with the aim of enhancing employee competency and achieving the highest possible organizational goals. Therefore, the purpose of this study is to investigate the potential impact of AI experience, AI utilization, AI technical ability, and AI app usefulness on employee self-competence performance in Jordanian pharmaceutical manufacturers. The variables under investigation included AI experience (X1), AI utilization (X2), AI technical ability (X3), AI app usefulness (X4), and individual competency performance (Y). This study employs a purposive sampling technique to gather quantitative data. The SPSS software application analyzes the primary data collected through a questionnaire in this study. The findings show that the AI experience, AI utilization, AI technical ability, and AI app usefulness have a significant positive effect on employee self-competence performance. The findings of this study have implications for stakeholders seeking to enhance individual manufacturing performance. This research identifies the effectiveness of AI experience, use, technical proficiency, and application utility as crucial factors influencing employee performance. These findings provide vital insights for business leaders seeking to improve productivity and operational efficiency. Furthermore, these findings provide a robust basis for evaluating performance and enhancing decision-making processes.https://doi.org/10.1007/s43621-025-01438-9Artificial intelligenceExperienceUtilizationTechnical abilityAppsManufacturing |
| spellingShingle | Anas Amayreh Mohammad A. Ta’Amnha Ihab K. Magableh Maher H. Mahrouq Salsabila Aisyah Alfaiza Exploring the impact of AI on employee self-competence performance key variables and outcomes Discover Sustainability Artificial intelligence Experience Utilization Technical ability Apps Manufacturing |
| title | Exploring the impact of AI on employee self-competence performance key variables and outcomes |
| title_full | Exploring the impact of AI on employee self-competence performance key variables and outcomes |
| title_fullStr | Exploring the impact of AI on employee self-competence performance key variables and outcomes |
| title_full_unstemmed | Exploring the impact of AI on employee self-competence performance key variables and outcomes |
| title_short | Exploring the impact of AI on employee self-competence performance key variables and outcomes |
| title_sort | exploring the impact of ai on employee self competence performance key variables and outcomes |
| topic | Artificial intelligence Experience Utilization Technical ability Apps Manufacturing |
| url | https://doi.org/10.1007/s43621-025-01438-9 |
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