Knowledge Management Performance Research Articles

Knowledge Management and Employee Performance in Nigeria Money Deposit Banks: A Case Study of Lagos State

Employees play a crucial role in advancing an organization's human and social capital, contributing significantly to knowledge creation and management, especially in the banking sector. Acknowledging their importance, the banking industry prioritizes improving its workforce. As a result, organizations, particularly those in Nigerian banking, invest resources in boosting employee performance through training, motivation, and career development. Despite these endeavours, there are still observable shortcomings in the performance of employees within Nigerian deposit money banks. The study adopted a survey research design. The population of the study consists of 4,873 employees of the eight selected deposit money banks in Lagos State, Nigeria. A sample size of 464 was determined, and data was collected using a valid and reliable questionnaire with a Cronbach alpha value ranging from 0.79 to 0.92. The data were analysed using both descriptive and inferential tools. A response rate of 87.2% was recorded. Simple linear regression analysis was used to determine the effect of the variables using Statistical Package for Social Science (SPSS) version 27. The findings revealed knowledge acquisition had a significant effect on employee performance in selected deposit money banks in Lagos State, Nigeria (Adj. R = 0.784; F(1, 404) = 64.762; p < 0.05), also, knowledge sharing had a significant effect on employee performance (Adj. R = 0.702; F(1, 404) = 390.478; p < 0.05), similarly, knowledge application had a significant effect on employee performance (Adj. R = 0.810; F(1, 404) = 769.885; p < 0.05). The study concluded that knowledge acquisition, knowledge sharing, and knowledge application have significant effects on employee performance. Based on these findings, it is recommended that deposit money banks in Nigeria invest in robust knowledge management programs and initiatives. These programs should focus on enhancing employees' abilities to acquire, share, and apply knowledge effectively. Implementing training sessions, workshops, and knowledge-sharing platforms can facilitate a culture of continuous learning and collaboration within the organization.

The Interplay of Knowledge Management, E-Governance, and Business Performance Efficiency: Insights from Saudi Public Sector Institutions

This study goes more deeply into the critical aspects of understanding knowledge management, e-governance, and performance efficiency in the Saudi public sector. Simultaneously, the main target is first to grasp how these aspects influence this kind of effectiveness, especially in light of this significant Vision 2030 of the State. By utilizing a technique, the research exploited determined it utilitarian of SMART PLS software to model Structural Equation Modeling (SEM) structure. The study involved 170 people, from the highest-level executives to entry-level employees and cross-section management, administration, and IT specialists. The given article will pursue the complexity of the characters’ interactions while revealing some of the book’s central lessons. T for this study tool, an elaborate questionnaire went through an extensive pretest phase where the content validity and reliability were verified through interviews with expert methods. In 2023, over a quarter of a year data collection period, the self-administered questionnaire was distributed to the representatives of different public institutions, which was done during the year 2023. We obtained some necessary information through the data found and processed using the SMART PLS and SEM. Outcomes highlighted the complex and multidimensional interrelationship of knowledge management, e-governance, and business performance efficiency, from which we make the knowledge of various factors facilitating the performance of the public sector offices in Saudi Arabia. Indeed, it is worth mentioning that the research endorses essential elements in line with Vision 2030, adding tangible directions to the policymakers, government leaders, and practitioners aiming to increase the productivity and effectiveness of public sector projects in the Kingdom.

RETRACTED ARTICLE: Financing Support Strategy of Green Manufacturer for Financially Constrained Retailer

Retraction statement We, the Editors and Publisher of the journal Knowledge Management Research & Practice (the “Journal”), have retracted the following articles that were published in the Special Issue titled “ The Impact of Artificial Intelligence on Knowledge Management and Business Strategic Planning ” guest-edited by Gunasekaran Manogaran: Fu, C., Jiang, H., & Chen, X. (2022). Modeling of an Enterprise Knowledge Management System Based on Artificial Intelligence. Knowledge Management Research & Practice, 1–13. https://doi.org/10.1080/14778238.2020.1854632 Wu, H., Gu, X., Zhao, Y., & Liu, W. (2020). Research on the Relationship between Structural Hole Location, Knowledge Management and Cooperative Innovation Performance in Artificial Intelligence. Knowledge Management Research & Practice, 1–10. https://doi.org/10.1080/14778238.2020.1813642 Wang, M. (2020). Financing support strategy of green manufacturer for financially constrained retailer. Knowledge Management Research & Practice, 1–15. https://doi.org/10.1080/14778238.2020.1771224 Elashkar, E., Aldeek, F., & Shoukry, A. (2020). Business predictive analysis from business insurance data using business strategic planning techniques. Knowledge Management Research & Practice, 1–10. https://doi.org/10.1080/14778238.2020.1776172 Huang, Z., He, J., & Ren, X. (2021). Application of artificial intelligence in enterprise knowledge management performance evaluation. Knowledge Management Research & Practice, 1–9. https://doi.org/10.1080/14778238.2020.1850187 Lei, Z., & Wang, L. (2020). Construction of organisational system of enterprise knowledge management networking module based on artificial intelligence. Knowledge Management Research & Practice, 1–13. https://doi.org/10.1080/14778238.2020.1831892 Li, Z., & Mo, T. (2020). Early warning of engineering project knowledge management risk based on artificial intelligence. Knowledge Management Research & Practice, 1–11. https://doi.org/10.1080/14778238.2020.1834885 Yang, J., Wen, C., & Gan, L. (2021). Model of an Enterprise Knowledge Chain Management System Based on Multiagent Technology in Distributed Artificial Intelligence. Knowledge Management Research & Practice, 1–12. https://doi.org/10.1080/14778238.2020.1850188 Bai, X., & Li, J. (2020). The best configuration of collaborative knowledge innovation management from the perspective of artificial intelligence. Knowledge Management Research & Practice, 1–13. https://doi.org/10.1080/14778238.2020.1834886 Following publication, the Publisher identified concerns regarding the editorial handling of the Special Issue and the peer review process. An investigation by the Taylor & Francis Publishing Ethics & Integrity team in full cooperation with the Editors-in-Chief concluded that the articles included in this Special Issue were not peer-reviewed appropriately, in line with the Journal’s peer review standards and policy. As the stringency of the peer review process is core to the integrity of the publication process, the Editors-in-Chief and Publisher have decided to retract all of the articles within the above-named Special Issue. The Editors-in-Chief and the Publisher have not confirmed if the authors were aware of this compromised peer review process. The Journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this decision. We have been informed in our decision-making by our editorial policies and the COPE guidelines. The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as ‘Retracted’. © 2019 Informa UK Limited, trading as Taylor & Francis Group/Journal owner

RETRACTED ARTICLE: Research on the Relationship between Structural Hole Location, Knowledge Management and Cooperative Innovation Performance in Artificial Intelligence

Retraction statement We, the Editors and Publisher of the journal Knowledge Management Research & Practice (the “Journal”), have retracted the following articles that were published in the Special Issue titled “ The Impact of Artificial Intelligence on Knowledge Management and Business Strategic Planning ” guest-edited by Gunasekaran Manogaran: Fu, C., Jiang, H., & Chen, X. (2022). Modeling of an Enterprise Knowledge Management System Based on Artificial Intelligence. Knowledge Management Research & Practice, 1–13. https://doi.org/10.1080/14778238.2020.1854632 Wu, H., Gu, X., Zhao, Y., & Liu, W. (2020). Research on the Relationship between Structural Hole Location, Knowledge Management and Cooperative Innovation Performance in Artificial Intelligence. Knowledge Management Research & Practice, 1–10. https://doi.org/10.1080/14778238.2020.1813642 Wang, M. (2020). Financing support strategy of green manufacturer for financially constrained retailer. Knowledge Management Research & Practice, 1–15. https://doi.org/10.1080/14778238.2020.1771224 Elashkar, E., Aldeek, F., & Shoukry, A. (2020). Business predictive analysis from business insurance data using business strategic planning techniques. Knowledge Management Research & Practice, 1–10. https://doi.org/10.1080/14778238.2020.1776172 Huang, Z., He, J., & Ren, X. (2021). Application of artificial intelligence in enterprise knowledge management performance evaluation. Knowledge Management Research & Practice, 1–9. https://doi.org/10.1080/14778238.2020.1850187 Lei, Z., & Wang, L. (2020). Construction of organisational system of enterprise knowledge management networking module based on artificial intelligence. Knowledge Management Research & Practice, 1–13. https://doi.org/10.1080/14778238.2020.1831892 Li, Z., & Mo, T. (2020). Early warning of engineering project knowledge management risk based on artificial intelligence. Knowledge Management Research & Practice, 1–11. https://doi.org/10.1080/14778238.2020.1834885 Yang, J., Wen, C., & Gan, L. (2021). Model of an Enterprise Knowledge Chain Management System Based on Multiagent Technology in Distributed Artificial Intelligence. Knowledge Management Research & Practice, 1–12. https://doi.org/10.1080/14778238.2020.1850188 Bai, X., & Li, J. (2020). The best configuration of collaborative knowledge innovation management from the perspective of artificial intelligence. Knowledge Management Research & Practice, 1–13. https://doi.org/10.1080/14778238.2020.1834886 Following publication, the Publisher identified concerns regarding the editorial handling of the Special Issue and the peer review process. An investigation by the Taylor & Francis Publishing Ethics & Integrity team in full cooperation with the Editors-in-Chief concluded that the articles included in this Special Issue were not peer-reviewed appropriately, in line with the Journal’s peer review standards and policy. As the stringency of the peer review process is core to the integrity of the publication process, the Editors-in-Chief and Publisher have decided to retract all of the articles within the above-named Special Issue. The Editors-in-Chief and the Publisher have not confirmed if the authors were aware of this compromised peer review process. The Journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this decision. We have been informed in our decision-making by our editorial policies and the COPE guidelines. The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as ‘Retracted’. © 2019 Informa UK Limited, trading as Taylor & Francis Group/Journal owner

RETRACTED ARTICLE: The best configuration of collaborative knowledge innovation management from the perspective of artificial intelligence

Retraction statement We, the Editors and Publisher of the journal Knowledge Management Research & Practice (the “Journal”), have retracted the following articles that were published in the Special Issue titled “ The Impact of Artificial Intelligence on Knowledge Management and Business Strategic Planning ” guest-edited by Gunasekaran Manogaran: Fu, C., Jiang, H., & Chen, X. (2022). Modeling of an Enterprise Knowledge Management System Based on Artificial Intelligence. Knowledge Management Research & Practice, 1–13. https://doi.org/10.1080/14778238.2020.1854632 Wu, H., Gu, X., Zhao, Y., & Liu, W. (2020). Research on the Relationship between Structural Hole Location, Knowledge Management and Cooperative Innovation Performance in Artificial Intelligence. Knowledge Management Research & Practice, 1–10. https://doi.org/10.1080/14778238.2020.1813642 Wang, M. (2020). Financing support strategy of green manufacturer for financially constrained retailer. Knowledge Management Research & Practice, 1–15. https://doi.org/10.1080/14778238.2020.1771224 Elashkar, E., Aldeek, F., & Shoukry, A. (2020). Business predictive analysis from business insurance data using business strategic planning techniques. Knowledge Management Research & Practice, 1–10. https://doi.org/10.1080/14778238.2020.1776172 Huang, Z., He, J., & Ren, X. (2021). Application of artificial intelligence in enterprise knowledge management performance evaluation. Knowledge Management Research & Practice, 1–9. https://doi.org/10.1080/14778238.2020.1850187 Lei, Z., & Wang, L. (2020). Construction of organisational system of enterprise knowledge management networking module based on artificial intelligence. Knowledge Management Research & Practice, 1–13. https://doi.org/10.1080/14778238.2020.1831892 Li, Z., & Mo, T. (2020). Early warning of engineering project knowledge management risk based on artificial intelligence. Knowledge Management Research & Practice, 1–11. https://doi.org/10.1080/14778238.2020.1834885 Yang, J., Wen, C., & Gan, L. (2021). Model of an Enterprise Knowledge Chain Management System Based on Multiagent Technology in Distributed Artificial Intelligence. Knowledge Management Research & Practice, 1–12. https://doi.org/10.1080/14778238.2020.1850188 Bai, X., & Li, J. (2020). The best configuration of collaborative knowledge innovation management from the perspective of artificial intelligence. Knowledge Management Research & Practice, 1–13. https://doi.org/10.1080/14778238.2020.1834886 Following publication, the Publisher identified concerns regarding the editorial handling of the Special Issue and the peer review process. An investigation by the Taylor & Francis Publishing Ethics & Integrity team in full cooperation with the Editors-in-Chief concluded that the articles included in this Special Issue were not peer-reviewed appropriately, in line with the Journal’s peer review standards and policy. As the stringency of the peer review process is core to the integrity of the publication process, the Editors-in-Chief and Publisher have decided to retract all of the articles within the above-named Special Issue. The Editors-in-Chief and the Publisher have not confirmed if the authors were aware of this compromised peer review process. The Journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this decision. We have been informed in our decision-making by our editorial policies and the COPE guidelines. The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as ‘Retracted’. © 2019 Informa UK Limited, trading as Taylor & Francis Group/Journal owner

RETRACTED ARTICLE: Construction of Organisational System of Enterprise Knowledge Management Networking Module Based on Artificial Intelligence

Retraction statement We, the Editors and Publisher of the journal Knowledge Management Research & Practice (the “Journal”), have retracted the following articles that were published in the Special Issue titled “ The Impact of Artificial Intelligence on Knowledge Management and Business Strategic Planning ” guest-edited by Gunasekaran Manogaran: Fu, C., Jiang, H., & Chen, X. (2022). Modeling of an Enterprise Knowledge Management System Based on Artificial Intelligence. Knowledge Management Research & Practice, 1–13. https://doi.org/10.1080/14778238.2020.1854632 Wu, H., Gu, X., Zhao, Y., & Liu, W. (2020). Research on the Relationship between Structural Hole Location, Knowledge Management and Cooperative Innovation Performance in Artificial Intelligence. Knowledge Management Research & Practice, 1–10. https://doi.org/10.1080/14778238.2020.1813642 Wang, M. (2020). Financing support strategy of green manufacturer for financially constrained retailer. Knowledge Management Research & Practice, 1–15. https://doi.org/10.1080/14778238.2020.1771224 Elashkar, E., Aldeek, F., & Shoukry, A. (2020). Business predictive analysis from business insurance data using business strategic planning techniques. Knowledge Management Research & Practice, 1–10. https://doi.org/10.1080/14778238.2020.1776172 Huang, Z., He, J., & Ren, X. (2021). Application of artificial intelligence in enterprise knowledge management performance evaluation. Knowledge Management Research & Practice, 1–9. https://doi.org/10.1080/14778238.2020.1850187 Lei, Z., & Wang, L. (2020). Construction of organisational system of enterprise knowledge management networking module based on artificial intelligence. Knowledge Management Research & Practice, 1–13. https://doi.org/10.1080/14778238.2020.1831892 Li, Z., & Mo, T. (2020). Early warning of engineering project knowledge management risk based on artificial intelligence. Knowledge Management Research & Practice, 1–11. https://doi.org/10.1080/14778238.2020.1834885 Yang, J., Wen, C., & Gan, L. (2021). Model of an Enterprise Knowledge Chain Management System Based on Multiagent Technology in Distributed Artificial Intelligence. Knowledge Management Research & Practice, 1–12. https://doi.org/10.1080/14778238.2020.1850188 Bai, X., & Li, J. (2020). The best configuration of collaborative knowledge innovation management from the perspective of artificial intelligence. Knowledge Management Research & Practice, 1–13. https://doi.org/10.1080/14778238.2020.1834886 Following publication, the Publisher identified concerns regarding the editorial handling of the Special Issue and the peer review process. An investigation by the Taylor & Francis Publishing Ethics & Integrity team in full cooperation with the Editors-in-Chief concluded that the articles included in this Special Issue were not peer-reviewed appropriately, in line with the Journal’s peer review standards and policy. As the stringency of the peer review process is core to the integrity of the publication process, the Editors-in-Chief and Publisher have decided to retract all of the articles within the above-named Special Issue. The Editors-in-Chief and the Publisher have not confirmed if the authors were aware of this compromised peer review process. The Journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this decision. We have been informed in our decision-making by our editorial policies and the COPE guidelines. The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as ‘Retracted’. © 2019 Informa UK Limited, trading as Taylor & Francis Group/Journal owner

RETRACTED ARTICLE: Early warning of engineering project knowledge management risk based on artificial intelligence

Retraction statement We, the Editors and Publisher of the journal Knowledge Management Research & Practice (the “Journal”), have retracted the following articles that were published in the Special Issue titled “ The Impact of Artificial Intelligence on Knowledge Management and Business Strategic Planning ” guest-edited by Gunasekaran Manogaran: Fu, C., Jiang, H., & Chen, X. (2022). Modeling of an Enterprise Knowledge Management System Based on Artificial Intelligence. Knowledge Management Research & Practice, 1–13. https://doi.org/10.1080/14778238.2020.1854632 Wu, H., Gu, X., Zhao, Y., & Liu, W. (2020). Research on the Relationship between Structural Hole Location, Knowledge Management and Cooperative Innovation Performance in Artificial Intelligence. Knowledge Management Research & Practice, 1–10. https://doi.org/10.1080/14778238.2020.1813642 Wang, M. (2020). Financing support strategy of green manufacturer for financially constrained retailer. Knowledge Management Research & Practice, 1–15. https://doi.org/10.1080/14778238.2020.1771224 Elashkar, E., Aldeek, F., & Shoukry, A. (2020). Business predictive analysis from business insurance data using business strategic planning techniques. Knowledge Management Research & Practice, 1–10. https://doi.org/10.1080/14778238.2020.1776172 Huang, Z., He, J., & Ren, X. (2021). Application of artificial intelligence in enterprise knowledge management performance evaluation. Knowledge Management Research & Practice, 1–9. https://doi.org/10.1080/14778238.2020.1850187 Lei, Z., & Wang, L. (2020). Construction of organisational system of enterprise knowledge management networking module based on artificial intelligence. Knowledge Management Research & Practice, 1–13. https://doi.org/10.1080/14778238.2020.1831892 Li, Z., & Mo, T. (2020). Early warning of engineering project knowledge management risk based on artificial intelligence. Knowledge Management Research & Practice, 1–11. https://doi.org/10.1080/14778238.2020.1834885 Yang, J., Wen, C., & Gan, L. (2021). Model of an Enterprise Knowledge Chain Management System Based on Multiagent Technology in Distributed Artificial Intelligence. Knowledge Management Research & Practice, 1–12. https://doi.org/10.1080/14778238.2020.1850188 Bai, X., & Li, J. (2020). The best configuration of collaborative knowledge innovation management from the perspective of artificial intelligence. Knowledge Management Research & Practice, 1–13. https://doi.org/10.1080/14778238.2020.1834886 Following publication, the Publisher identified concerns regarding the editorial handling of the Special Issue and the peer review process. An investigation by the Taylor & Francis Publishing Ethics & Integrity team in full cooperation with the Editors-in-Chief concluded that the articles included in this Special Issue were not peer-reviewed appropriately, in line with the Journal’s peer review standards and policy. As the stringency of the peer review process is core to the integrity of the publication process, the Editors-in-Chief and Publisher have decided to retract all of the articles within the above-named Special Issue. The Editors-in-Chief and the Publisher have not confirmed if the authors were aware of this compromised peer review process. The Journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this decision. We have been informed in our decision-making by our editorial policies and the COPE guidelines. The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as ‘Retracted’. © 2019 Informa UK Limited, trading as Taylor & Francis Group/Journal owner

RETRACTED ARTICLE: Business predictive analysis from business insurance data using business strategic planning techniques

ABSTRACT Retraction statement We, the Editors and Publisher of the journal Knowledge Management Research & Practice (the “Journal”), have retracted the following articles that were published in the Special Issue titled “ The Impact of Artificial Intelligence on Knowledge Management and Business Strategic Planning ” guest-edited by Gunasekaran Manogaran: Fu, C., Jiang, H., & Chen, X. (2022). Modeling of an Enterprise Knowledge Management System Based on Artificial Intelligence. Knowledge Management Research & Practice, 1–13. https://doi.org/10.1080/14778238.2020.1854632 Wu, H., Gu, X., Zhao, Y., & Liu, W. (2020). Research on the Relationship between Structural Hole Location, Knowledge Management and Cooperative Innovation Performance in Artificial Intelligence. Knowledge Management Research & Practice, 1–10. https://doi.org/10.1080/14778238.2020.1813642 Wang, M. (2020). Financing support strategy of green manufacturer for financially constrained retailer. Knowledge Management Research & Practice, 1–15. https://doi.org/10.1080/14778238.2020.1771224 Elashkar, E., Aldeek, F., & Shoukry, A. (2020). Business predictive analysis from business insurance data using business strategic planning techniques. Knowledge Management Research & Practice, 1–10. https://doi.org/10.1080/14778238.2020.1776172 Huang, Z., He, J., & Ren, X. (2021). Application of artificial intelligence in enterprise knowledge management performance evaluation. Knowledge Management Research & Practice, 1–9. https://doi.org/10.1080/14778238.2020.1850187 Lei, Z., & Wang, L. (2020). Construction of organisational system of enterprise knowledge management networking module based on artificial intelligence. Knowledge Management Research & Practice, 1–13. https://doi.org/10.1080/14778238.2020.1831892 Li, Z., & Mo, T. (2020). Early warning of engineering project knowledge management risk based on artificial intelligence. Knowledge Management Research & Practice, 1–11. https://doi.org/10.1080/14778238.2020.1834885 Yang, J., Wen, C., & Gan, L. (2021). Model of an Enterprise Knowledge Chain Management System Based on Multiagent Technology in Distributed Artificial Intelligence. Knowledge Management Research & Practice, 1–12. https://doi.org/10.1080/14778238.2020.1850188 Bai, X., & Li, J. (2020). The best configuration of collaborative knowledge innovation management from the perspective of artificial intelligence. Knowledge Management Research & Practice, 1–13. https://doi.org/10.1080/14778238.2020.1834886 Following publication, the Publisher identified concerns regarding the editorial handling of the Special Issue and the peer review process. An investigation by the Taylor & Francis Publishing Ethics & Integrity team in full cooperation with the Editors-in-Chief concluded that the articles included in this Special Issue were not peer-reviewed appropriately, in line with the Journal’s peer review standards and policy. As the stringency of the peer review process is core to the integrity of the publication process, the Editors-in-Chief and Publisher have decided to retract all of the articles within the above-named Special Issue. The Editors-in-Chief and the Publisher have not confirmed if the authors were aware of this compromised peer review process. The Journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this decision. We have been informed in our decision-making by our editorial policies and the COPE guidelines. The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as ‘Retracted’. © 2019 Informa UK Limited, trading as Taylor & Francis Group/Journal owner

RETRACTED ARTICLE: Modeling of an Enterprise Knowledge Management System Based on Artificial Intelligence

Retraction statement We, the Editors and Publisher of the journal Knowledge Management Research & Practice (the “Journal”), have retracted the following articles that were published in the Special Issue titled “ The Impact of Artificial Intelligence on Knowledge Management and Business Strategic Planning ” guest-edited by Gunasekaran Manogaran: Fu, C., Jiang, H., & Chen, X. (2022). Modeling of an Enterprise Knowledge Management System Based on Artificial Intelligence. Knowledge Management Research & Practice, 1–13. https://doi.org/10.1080/14778238.2020.1854632 Wu, H., Gu, X., Zhao, Y., & Liu, W. (2020). Research on the Relationship between Structural Hole Location, Knowledge Management and Cooperative Innovation Performance in Artificial Intelligence. Knowledge Management Research & Practice, 1–10. https://doi.org/10.1080/14778238.2020.1813642 Wang, M. (2020). Financing support strategy of green manufacturer for financially constrained retailer. Knowledge Management Research & Practice, 1–15. https://doi.org/10.1080/14778238.2020.1771224 Elashkar, E., Aldeek, F., & Shoukry, A. (2020). Business predictive analysis from business insurance data using business strategic planning techniques. Knowledge Management Research & Practice, 1–10. https://doi.org/10.1080/14778238.2020.1776172 Huang, Z., He, J., & Ren, X. (2021). Application of artificial intelligence in enterprise knowledge management performance evaluation. Knowledge Management Research & Practice, 1–9. https://doi.org/10.1080/14778238.2020.1850187 Lei, Z., & Wang, L. (2020). Construction of organisational system of enterprise knowledge management networking module based on artificial intelligence. Knowledge Management Research & Practice, 1–13. https://doi.org/10.1080/14778238.2020.1831892 Li, Z., & Mo, T. (2020). Early warning of engineering project knowledge management risk based on artificial intelligence. Knowledge Management Research & Practice, 1–11. https://doi.org/10.1080/14778238.2020.1834885 Yang, J., Wen, C., & Gan, L. (2021). Model of an Enterprise Knowledge Chain Management System Based on Multiagent Technology in Distributed Artificial Intelligence. Knowledge Management Research & Practice, 1–12. https://doi.org/10.1080/14778238.2020.1850188 Bai, X., & Li, J. (2020). The best configuration of collaborative knowledge innovation management from the perspective of artificial intelligence. Knowledge Management Research & Practice, 1–13. https://doi.org/10.1080/14778238.2020.1834886 Following publication, the Publisher identified concerns regarding the editorial handling of the Special Issue and the peer review process. An investigation by the Taylor & Francis Publishing Ethics & Integrity team in full cooperation with the Editors-in-Chief concluded that the articles included in this Special Issue were not peer-reviewed appropriately, in line with the Journal’s peer review standards and policy. As the stringency of the peer review process is core to the integrity of the publication process, the Editors-in-Chief and Publisher have decided to retract all of the articles within the above-named Special Issue. The Editors-in-Chief and the Publisher have not confirmed if the authors were aware of this compromised peer review process. The Journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this decision. We have been informed in our decision-making by our editorial policies and the COPE guidelines. The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as ‘Retracted’. © 2019 Informa UK Limited, trading as Taylor & Francis Group/Journal owner

RETRACTED ARTICLE: Model of an Enterprise Knowledge Chain Management System Based on Multiagent Technology in Distributed Artificial Intelligence

Retraction statement We, the Editors and Publisher of the journal Knowledge Management Research & Practice (the “Journal”), have retracted the following articles that were published in the Special Issue titled “ The Impact of Artificial Intelligence on Knowledge Management and Business Strategic Planning ” guest-edited by Gunasekaran Manogaran: Fu, C., Jiang, H., & Chen, X. (2022). Modeling of an Enterprise Knowledge Management System Based on Artificial Intelligence. Knowledge Management Research & Practice, 1–13. https://doi.org/10.1080/14778238.2020.1854632 Wu, H., Gu, X., Zhao, Y., & Liu, W. (2020). Research on the Relationship between Structural Hole Location, Knowledge Management and Cooperative Innovation Performance in Artificial Intelligence. Knowledge Management Research & Practice, 1–10. https://doi.org/10.1080/14778238.2020.1813642 Wang, M. (2020). Financing support strategy of green manufacturer for financially constrained retailer. Knowledge Management Research & Practice, 1–15. https://doi.org/10.1080/14778238.2020.1771224 Elashkar, E., Aldeek, F., & Shoukry, A. (2020). Business predictive analysis from business insurance data using business strategic planning techniques. Knowledge Management Research & Practice, 1–10. https://doi.org/10.1080/14778238.2020.1776172 Huang, Z., He, J., & Ren, X. (2021). Application of artificial intelligence in enterprise knowledge management performance evaluation. Knowledge Management Research & Practice, 1–9. https://doi.org/10.1080/14778238.2020.1850187 Lei, Z., & Wang, L. (2020). Construction of organisational system of enterprise knowledge management networking module based on artificial intelligence. Knowledge Management Research & Practice, 1–13. https://doi.org/10.1080/14778238.2020.1831892 Li, Z., & Mo, T. (2020). Early warning of engineering project knowledge management risk based on artificial intelligence. Knowledge Management Research & Practice, 1–11. https://doi.org/10.1080/14778238.2020.1834885 Yang, J., Wen, C., & Gan, L. (2021). Model of an Enterprise Knowledge Chain Management System Based on Multiagent Technology in Distributed Artificial Intelligence. Knowledge Management Research & Practice, 1–12. https://doi.org/10.1080/14778238.2020.1850188 Bai, X., & Li, J. (2020). The best configuration of collaborative knowledge innovation management from the perspective of artificial intelligence. Knowledge Management Research & Practice, 1–13. https://doi.org/10.1080/14778238.2020.1834886 Following publication, the Publisher identified concerns regarding the editorial handling of the Special Issue and the peer review process. An investigation by the Taylor & Francis Publishing Ethics & Integrity team in full cooperation with the Editors-in-Chief concluded that the articles included in this Special Issue were not peer-reviewed appropriately, in line with the Journal’s peer review standards and policy. As the stringency of the peer review process is core to the integrity of the publication process, the Editors-in-Chief and Publisher have decided to retract all of the articles within the above-named Special Issue. The Editors-in-Chief and the Publisher have not confirmed if the authors were aware of this compromised peer review process. The Journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this decision. We have been informed in our decision-making by our editorial policies and the COPE guidelines. The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as ‘Retracted’. © 2019 Informa UK Limited, trading as Taylor & Francis Group/Journal owner

RETRACTED ARTICLE: Application of artificial intelligence in enterprise knowledge management performance evaluation

Retraction statement We, the Editors and Publisher of the journal Knowledge Management Research & Practice (the “Journal”), have retracted the following articles that were published in the Special Issue titled “ The Impact of Artificial Intelligence on Knowledge Management and Business Strategic Planning ” guest-edited by Gunasekaran Manogaran: Fu, C., Jiang, H., & Chen, X. (2022). Modeling of an Enterprise Knowledge Management System Based on Artificial Intelligence. Knowledge Management Research & Practice, 1–13. https://doi.org/10.1080/14778238.2020.1854632 Wu, H., Gu, X., Zhao, Y., & Liu, W. (2020). Research on the Relationship between Structural Hole Location, Knowledge Management and Cooperative Innovation Performance in Artificial Intelligence. Knowledge Management Research & Practice, 1–10. https://doi.org/10.1080/14778238.2020.1813642 Wang, M. (2020). Financing support strategy of green manufacturer for financially constrained retailer. Knowledge Management Research & Practice, 1–15. https://doi.org/10.1080/14778238.2020.1771224 Elashkar, E., Aldeek, F., & Shoukry, A. (2020). Business predictive analysis from business insurance data using business strategic planning techniques. Knowledge Management Research & Practice, 1–10. https://doi.org/10.1080/14778238.2020.1776172 Huang, Z., He, J., & Ren, X. (2021). Application of artificial intelligence in enterprise knowledge management performance evaluation. Knowledge Management Research & Practice, 1–9. https://doi.org/10.1080/14778238.2020.1850187 Lei, Z., & Wang, L. (2020). Construction of organisational system of enterprise knowledge management networking module based on artificial intelligence. Knowledge Management Research & Practice, 1–13. https://doi.org/10.1080/14778238.2020.1831892 Li, Z., & Mo, T. (2020). Early warning of engineering project knowledge management risk based on artificial intelligence. Knowledge Management Research & Practice, 1–11. https://doi.org/10.1080/14778238.2020.1834885 Yang, J., Wen, C., & Gan, L. (2021). Model of an Enterprise Knowledge Chain Management System Based on Multiagent Technology in Distributed Artificial Intelligence. Knowledge Management Research & Practice, 1–12. https://doi.org/10.1080/14778238.2020.1850188 Bai, X., & Li, J. (2020). The best configuration of collaborative knowledge innovation management from the perspective of artificial intelligence. Knowledge Management Research & Practice, 1–13. https://doi.org/10.1080/14778238.2020.1834886 Following publication, the Publisher identified concerns regarding the editorial handling of the Special Issue and the peer review process. An investigation by the Taylor & Francis Publishing Ethics & Integrity team in full cooperation with the Editors-in-Chief concluded that the articles included in this Special Issue were not peer-reviewed appropriately, in line with the Journal’s peer review standards and policy. As the stringency of the peer review process is core to the integrity of the publication process, the Editors-in-Chief and Publisher have decided to retract all of the articles within the above-named Special Issue. The Editors-in-Chief and the Publisher have not confirmed if the authors were aware of this compromised peer review process. The Journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this decision. We have been informed in our decision-making by our editorial policies and the COPE guidelines. The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as ‘Retracted’. © 2019 Informa UK Limited, trading as Taylor & Francis Group/Journal owner

Investigation the Influence of Job Satisfaction as a Mediating Factor in the Relationship Between Transformational Leadership, Knowledge, Knowledge Management, Work Environment and Employee Performance

Objective: This research aims to assess job satisfaction's mediating role between transformational leadership (TL), work environment (WE), knowledge management (KM) practices, and employee performance (EP) by investigating TL's and WE's impact on job satisfaction, and examining how job satisfaction mediates the relationships between TL, KM practices, and EP. Theoretical Framework: The theoretical framework examines key factors affecting employee performance and organizational success. It highlights task and contextual performance, environmental influences, and the role of performance appraisals. Effective human resource practices, knowledge management, and transformational leadership are essential for high performance. Job satisfaction, influenced by knowledge management and leadership, enhances organizational success. Method: This quantitative study investigates the relationships among transformational leadership (TL), work environment (WE), job satisfaction (JS), knowledge management (KM) practices, and employee performance (EP). Data from 411 Thai-speaking participants are analyzed using structural equation modeling (SEM). The study employs validated questionnaires, a 5-point Likert scale, and ADANCO software to assess model fit, reliability, and validity. Results: The sample predominantly comprised females (64.7%) and individuals with bachelor’s degrees (59.12%), with 31.87% employed in companies and 41.12% earning between 30,001 and 50,000 baht. Descriptive analysis revealed strong positive perceptions of KM, TL, WE, JS, and EP, with mean scores between 4.74 and 4.87, underscoring the focus on EP. Structural equation modeling (SEM) identified significant relationships between KM and JS (0.0277), TL and JS (0.0533), WE and JS (0.1720), and JS and EP (0.4721). JS emerged as a key mediator between KM, TL, and WE, accounting for substantial variance in both JS and EP, reflected by R² values of 0.0385 and 0.3283. Although the KM-JS and TL-JS relationships were relatively weak, JS significantly influenced EP. The study advocates for enhancing KM, TL, and WE to improve JS and EP, while recommending future research to investigate moderating factors, longitudinal designs, and cross-cultural contexts. Research Implications: The study reveals critical insights into job satisfaction (JS) as a mediator between transformational leadership (TL), knowledge management (KM), work environment (WE), and employee performance (EP): TL significantly influences KM and WE, while effective KM enhances EP. JS mediates the positive effects of TL, KM, and WE on EP, though moderate correlations suggest other influencing factors. The research emphasizes contextual nuances within Thai organizations, underscoring the complexity of these relationships and the need for further investigation to fully understand their organizational implications. Originality/Value: This study offers innovative insights into the interactions among Knowledge Management (KM), Transformational Leadership (TL), Work Environment (WE), Job Satisfaction (JS), and Employee Performance (EP) in Thai organizations. It identifies JS as a key mediator in the effects of KM, TL, and WE on EP, deepening the understanding of organizational dynamics. Moderate correlations between KM and JS, and TL and JS, suggest additional moderating factors. The pivotal role of JS in determining EP emphasizes the need for organizations to enhance JS to improve performance. The study's focus on Thai organizations highlights cultural and contextual influences, bridging local practices with broader theoretical frameworks. Further investigation is needed to fully understand these complex dynamics.

Empowering knowledge management in enhancing the performance of higher education institutions in the era of industrial revolution 4.0: A mediating role of innovation

Education is the most important element of economic growth and development, Pakistan is full of resources, but improper use of resources leads to it being underdeveloped. Where appropriate education standards can help Pakistan overcome economic problems. Therefore, Pakistan has failed to establish new universities in the last two decades. On the other hand, the performance of the education sector of Pakistan is insignificant and nowhere exists in international rankings. Higher education institutions (HEIs) are progressively incorporating modern technologies and creative approaches to improve their performance in the context of Education 4.0. This study uses innovation as a mediating variable to examine the relationship between knowledge management (KM) and HEI performance. (KM), which includes the organized administration of an organization's knowledge resources, is essential for creating an atmosphere that supports ongoing education and development. Educational paradigms and operational efficiencies are being reshaped by innovation, which is defined by the application of technologies. This study aims to use knowledge management as a construct in enhancing HEIP, where innovation plays a contributing factor as a mediator. The unit of analysis was organization; therefore, the current study targeted 88 universities in Pakistan. The samples were selected via a stratified random sampling technique, and the data were analyzed via SMART PLS 4.0. The findings of the current study revealed that knowledge management in the era of Education 4.0 is a significant construct in enhancing HEIP. Similarly, the results of the mediation also revealed that innovation is a significant contributing factor to the progress of HIEIP. Furthermore, the research explores optimal methodologies and obstacles linked to the amalgamation of knowledge management and innovation, furnishing HEIs with an all-encompassing framework designed to prosper in the context of Education 4.0.

The translation is: "The relationship between knowledge management and institutional performance: The Central Library of the Iraqi University as a model

The translation is: "The relationship between knowledge management and institutional performance: The Central Library of the Iraqi University as a model

PERSONAL KNOWLEDGE MANAGEMENT AND EMPLOYEE PERFORMANCE NEXUS WITH THRIVING AT WORK AS A MEDIATING FACTOR IN A PUBLIC ORGANIZATION CONTEXT

This study examined the relationship between personal knowledge management and employee performance by accounting for the mediational effect of thriving at work. The study used a cross-sectional survey to obtain data from 204 academic staff randomly sampled from state-owned uni­versities in Delta State, Nigeria. The partial least squares method was applied to treat and analyze the data collected with the help of SmartPLS 4 software. The analysis revealed that knowledge sharing, creation, and acquisition were positively and significantly related to employee performance. Furthermore, the mediation role of thriving was also confirmed. In conclusion, personal knowledge management activities comprising knowledge sharing, knowledge creation, and knowledge acqui­sition, directly and indirectly, affected employee performance through thriving at work. The study recommended that public managers should incentivize and support the personalization of knowledge management activities across organizational levels, as it benefited thriving at work and employee performance, especially its adaptive aspects. Furthermore, personal knowledge management and thriving at work were driven or guided by knowledge, making organizations need to curate inter­ven­tions to improve knowledge.

Management Skills in Micro, Small and Medium-Sized Enterprises (SMEs): a Systematic Review

Objective: To analyze the level of knowledge generated by various authors on the managerial skills of SMEs between 2014 and 2024. Theoretical Framework: Managerial skills are very relevant for the strategic exercise of innovation and development in companies, where technical skills are not enough, and are composed of various knowledge, skills, attitudes and behaviors that a person requires to efficiently develop the responsibilities to lead a company (Leyva and Cavazos 2018; Leyva et al., 2017). Method: The research has a qualitative approach since the purpose is to present a general study on Managerial Skills in SMEs, aspects of scientific production are analyzed and evaluated, through research in the Scopus and Web of Science (WOS) databases. Results and Discussion: Managerial skills, management skills, human relations, such as business relations influence the leadership style of entrepreneurs, generating a positive and people-oriented impact to the extent that there are greater human and business interactions (Abdullah et al., 2023). Research Implications: The development of SME organizations today are involved in a fast-changing environment that requires strengthening managerial skills in order to achieve strategic business objectives effectively and efficiently. Originality/value: The research will contribute to a better understanding of business organizations and the importance of developing managerial skills, enhancing innovation, knowledge management and SME performance.

Pengaruh Lingkungan Eksternal, Kompetensi Sumber Daya Manusia Dan Knowledge Management Terhadap Kinerja Organisasi Dengan Inovasi Sebagai Variabel Mediasi (Studi Pada UMKM di Kelurahan Pagerharjo, Kabupaten Kulon Progo)

This study aims to investigate the relationships between the external environment, human resource competencies, knowledge management, and organizational performance with innovation as a mediating variable. Data were collected through direct distribution of questionnaires to 60 respondents who are stakeholders of Kalurahan Pagerharjo, Kulon Progo Regency, registered under the Badan Usaha Milik Desa (BUMDes). The analysis method used was SEM-PLS. The study found that the external environment has a non-significant direct influence on organizational performance, but it significantly affects organizational performance when innovation is considered as a mediating variable. This might be due to the industry where the organization operates not being strongly influenced by external environmental factors. Human resource competencies have a significant direct influence on organizational performance, but their influence on organizational performance with innovation as a mediating variable is not significant. The research revealed that despite high levels of human resource competencies and innovation, they are not sufficient to enhance organizational performance. Knowledge management significantly influences organizational performance, both directly and with innovation as a mediating variable. These findings diverge from previous research and suggest that other factors may impact the external environment and organizational performance.

The impact of business model innovation and knowledge management on firm performance: an emerging markets perspective

PurposeDespite the growing literature on business model innovation (BMI) and its implications for firm performance, the research on the antecedents of BMI is still at a budding stage. Therefore, the purpose of this paper is to bridge this gap by examining the causal relationships between knowledge management, BMI, competitive advantage, and firm performance.Design/methodology/approachA purposive sample of 300 micro, small, and medium enterprises was collected with more than 500 employees and total revenue between 50 and 500 million Indian Rupees, according to the Development Act, 2005. The collected data were analyzed using structural equation modeling through smart PLS 3.FindingsThe study’s findings show that knowledge management significantly influences BMI. This study also highlights the synergic impact of Knowledge management and BMI on firm performance. Furthermore, findings highlight the mediating role of competitive advantage in the relationship between knowledge management, BMI, and firm performance.Practical implicationsUnderstanding the impact of BMI and knowledge management is crucial for business owners, managers, and other stakeholders to plan for the continuous improvement of their businesses. Managers can secure a favorable position in the competitive market and improve organizational performance by paying attention to effectively managing their knowledge resources.Originality/valueThe study is one of the first few empirical efforts to investigate the causal relationships between knowledge management, BMI and firm performance. The study adds to the scant literature in this area by highlighting the mediating role of competitive advantage.

The Synthesis of Integrative Sustainable Capability to Indonesian Sustainable Eco-print Fashion

With a focus on small businesses that are oriented towards sustainable fashion in the Eco-print business, this research focuses on forming a synthesis of Integrative Sustainable Capability variables which are derived from a systematic process through the derivation process of Resources Based Theory and Strategic Orientation. The synthesis of Strategic Resources in the theory of Resources Based and Strategic Orientation leads to the construction of a new concept, namely Integrative Sustainable Capability as a competency to integrate environmental and social considerations to strategically acquire, combine and deploy the resources in business operations and cooperation included in knowledge management to generate performance values. The structured idea process method is used to synthesize novelty constructed in the Integrative Sustainable Capability. The Integrative Sustainable Capability is used as a mediator in the relationship between Knowledge Management Capabilities and Entrepreneurial Performance in the context of Eco-print small businesses. The conceptual model was formed by linking these three variables of Knowledge Management and Entrepreneurial Performance with Integrative Sustainable Capability as a mediation derived from the synthesis of novelty process in the context of Eco-print small business.

Green learning orientation and corporate environmental performance: the mediation role of green knowledge acquisition-management and the moderating role of CEO-gender

PurposeThe study aims to examine the role of green learning orientation, green knowledge acquisition and green knowledge management in fostering corporate environmental performance of small and medium-sized enterprises (SMEs) in China. In addition, this research assesses the moderating role of chief executive officer (CEO) gender between green knowledge management and corporate environmental performance. Finally, this study examines the sequential mediating role of green knowledge acquisition and green knowledge management.Design/methodology/approachThe study collected the data of 300 SMEs’ CEOs taken from five different provinces in China. The study used a partial least squares regression-based structural equation modelling technique.FindingsThe findings revealed that green learning orientation plays an important role in increasing SMEs’ corporate environmental performance. The results showed that green knowledge acquisition and green knowledge management serially and completely mediate the relationship between green learning orientation and corporate environmental performance.Originality/valueTo the best of the authors’ knowledge, this is the first study addressing the sequence of knowledge orientation, acquisition, management and results in terms of corporate environmental performance. Meanwhile, this study is the first to examine the indirect role of CEO gender on the relationship between green knowledge management and corporate environmental performance. As decisions are taken by the top management and CEO, especially in the case of SMEs, the role of top management and how well top management uses the knowledge acquired by the organization matters significantly.