Project Knowledge Research Articles

RETRACTED ARTICLE: Financing Support Strategy of Green Manufacturer for Financially Constrained Retailer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

Nurturing ideas, practices and knowledge circulation in child and family social work: results from a collaborative project

ABSTRACT This article reports from a research and development project aiming to improve support for parents with children in out-of-home care in Sweden. The project was carried out in collaboration between the authors and around 60 social workers organized in improvement teams during 2019–2021. The aim of the article is to explore the turnout in three of the participating teams. At the start of the project, social work with parents of placed children was described as ‘stuck in a rut’, in spite of numerous alerts on improvement needs. As parent support can concern a wide range of actions, and as there was no specific evidence-based programme to recommend, the collaborative project focused on nurturing new ideas and practices. In the initial phase, the teams formulated about 30 ideas covering new forms of support and improved prerequisites for a shared parenthood. A fourth of these ideas were turned into new practices that were tested in the services. The most frequent practices concerned information, crisis support and contact visits. The findings indicate that the breakthrough of new practices was dependent on a new translation of parents’ rights and parent support which resulted in a new understanding of how to perform support without outcompeting a child's perspective. The translation process was highly influenced by the project interactions and knowledge circulation in which service user voices seem to have been especially vital.

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

Competitiveness of domestic educational services on the international market

It is indicated that Ukraine, as a country that has a large number of effective educational institutions that educate leading specialists in their field, who are currently using their knowledge in projects around the world, should be in the top list of countries for exporting educational services in the world. With the beginning of the anti-terrorist operation in 2014, as well as with the beginning of full-scale military operations in 2022, the development of the export of educational services in Ukraine decreased, and therefore the place of Ukrainian educational services in the international market of educational services decreased as well. The corresponding state of affairs implies the need to develop mechanisms and ways to increase the level of competitiveness of Ukraine in the international market of educational services, which is relevant and requires research. It was noted that on the way to European integration, Ukraine adheres to a number of international standards, actively develops its economy, in particular, in the field of education. The scientific article is devoted to the study of issues of competitiveness of domestic educational services on the international market. The place of domestic educational services in the international market and the dynamics of the development of domestic educational services in the international market are determined. The particularities of the obstacles that currently do not allow the proper development of Ukrainian educational services on the international market of educational services are given. The reasons that stimulate the development of domestic educational services and stimulate the strengthening of the position of domestic educational services on the international market are determined. Features, general characteristics and risks of competitiveness of domestic services and factors influencing the competitiveness of services are determined. The features of the competitiveness of domestic educational services are given, as well as the dynamics of the corresponding competitiveness before and during the legal regime of martial law in Ukraine. The impact of martial law on the development of the competitiveness of domestic educational services is determined; the negative impact of martial law on the competitiveness of domestic educational services and ways to improve the situation are highlighted.

Precipitation changes during the last glacial period in the Ili Basin, northern Central Asia, as inferred from the records of loess dolomite

Understanding the climatic evolution in Central Asia (CA) and its drivers is crucial for informed decision-making and predicting global changes due to its significant contribution as a global dust source. Unfortunately, our current understanding of the pre-Holocene precipitation patterns in CA is lacking due to the limited availability of reliable proxy indicators, and our knowledge of future precipitation projections in the region, based on paleoclimatic dynamics, is also quite limited. In this study, we analyzed variations in carbonate and dolomite contents of a loess section in the Ili Basin, northern CA, to reveal precipitation changes during the last glacial period. The results showed that changes in carbonate minerals were mainly influenced by the source material supply, driven by reduced precipitation and eluviation during glacial period. We thereby established a precipitation index by removing the influence of provenance signals from the dolomite records. The index indicated lower precipitation during mid-marine isotope stage (MIS) 3 compared to MIS2, likely due to meridional shifts and intensity changes of the westerlies caused by changes in precession and obliquity, with precession playing a major role. Through the comparison of the precipitation index with the δ18O records of the Greenland ice core on a millennial timescale, it was observed that the precipitation in northern CA exhibited a positive correlation with the North Atlantic Oscillation (NAO) mode due to migration of the westerlies. By leveraging our understanding of orbital- and millennial-scale precipitation patterns, we utilized the random forest (RF) regression model and the autoregressive integrated moving average model to forecast precipitation changes for the upcoming 5000–10,000 years. The results indicated a variable pattern marked by a general upward trend, suggesting the possibility of favorable development of agricultural-based economies in the Ili River Valley. People should realize that some integrated measures are designed to improve resilience of agricultural sector in the region and enhance its capacity to adapt to challenges posed by climate change. However, more extensive research is necessary to verify these results through thorough examination and comparisons of loess sections in our research location.

Determining the critical factors for realising successful post-disaster reconstruction projects in the Caribbean

Business as usual project management practice may not be applicable in the recovery of the built environment following a disaster. Unfortunately, little research has been undertaken to understand the nature of post-disaster recovery projects and the requirements for their success in the Caribbean. The Caribbean islands' exposure and susceptibility to natural hazards make it essential to understand what constitutes successful post-disaster reconstruction. By drawing on end-users’ perspectives in four Caribbean islands (Antigua, Dominica, Grenada, St. Vincent) through a quantitative survey and factor analysis, this paper identifies critical factors that determine successful outcomes with post-disaster Caribbean recovery projects. The factor analysis results from the survey responses on 24 empirical success factors suggests four composite Caribbean specific critical success factors, namely, 1) effective management of project knowledge, 2) environment and ecosystem stewardship, 3) stakeholder engagements at multiple levels, and 4) efficient management of financial, social and human resources. The findings from this research can assist with minimising the recurring post-disaster reconstruction projects, project financing donors and managers with an understanding of the issues that matter most to the region success, and realising the end-users’ (the beneficiaries) expectations. Mainstreaming these factors into project planning and execution, and adopting resilience-oriented project management practices, augers well for managing Caribbean disaster reconstruction projects for greater success.

Exploring the Disruptive Digital Technologies (DDTs)in the Metro Manila Hotel Industry Towards an Expanded Business Market Plan

This study integrates disruptive digital technologies into your existing marketing plan, propelling your campaigns to exploring the innovative digital technologies in the Metro Manila Hotel Industry towards an expanded business market plan. Digital technologies encompass a vast array of tools and techniques that leverage advancements in artificial intelligence (AI), machine learning (ML), and data analytics. The organizations reconnect and redesign digital technologies; acquire new skills to innovate, learn, and reshape to progress digital technologies, while digital technologies change the classification of knowledge for productive and innovative projects. The objective of this study is to know the marketing demographics profile such as age, geographic location, educational level, occupation and income that will utilize information that's easily accessible in innovative digital technology; to explore the innovative digital technologies of e-commerce that being used or implemented in the Metro Manila hotel industry, such as: Artificial intelligence (AI), Virtual reality (VR), Blockchain, Internet of Things (IoT) and Hybrid or electric vehicles to use as shuttle or service vehicles; to study the impact on the innovative technologies in the hotel operations; to create market plan can be proposed in the hotel industry using innovative digital technologie; the difference of innovative digital technologies in today's global industry; and the correlation of exploring innovative digital technologies in the Metro Manila hotel industry towards an expanded business market plan. To be specific, innovative digital technologies have the potential to transform the hotel industry.

Competence in Unsustainability Resolution—A New Paradigm

Environmental unsustainability in coupled human–nature systems is accumulating. Yet, there is no accreditation requirement for unsustainability resolution competency in higher education. Thus, a new and complete representation of the pedagogy for unsustainability resolution competence has been induced, using what is already available and working. The nature of unsustainability problems points to collaboration and holism attitudes. Resolution requires social skills, namely participation, perspective taking, and the generation of social capital, and cognitive skills, namely project management, knowledge building, and modeling. Resolution is scaffolded in three successive steps during the collaborative process within a systems approach: (i) collapse complexity; (ii) select a path/trajectory; and (iii) operationalize a plan. The hierarchically cumulative abilities toward unsustainability resolution competence are to source data and information about the coupled human–nature system (SEARCH); simplify the dynamics of the human–nature system (SIMULATE); generate and test alternative paths and end points for the coupled human–nature system (STRATEGIZE); chose a favorable path among the available alternatives (SELECT); operationalize the favorable path into a plan (strategy–program–project) with measurable management and policy objectives (IMPLEMENT); and develop criteria/indicators to monitor and adjust when necessary the implementation of the plan toward system goals (STEER). For each one of these learning objectives, the Bloom’s taxonomy and a progression from behaviorist through cognitivist to constructivist tools apply. The development of mastery requires the comparison and contrast of many similar cases with the same unsustainability problem and project-based learning with specific cases for deep learning. In this way, it is the resolutions of unsustainability in human–nature systems that will be accumulating.

Utilizing blockchain to enhance project management information systems: insights into project portfolio success, knowledge management and learning capabilities

PurposeThis paper explores how blockchain technology can enhance information quality within project management information systems (PMIS), thereby positively affecting knowledge management, learning capabilities and project portfolio success.Design/methodology/approachWe employ a literature review and a theory-based approach to develop a conceptual framework and set of propositions that integrate key principles from blockchain technology, project management and dynamic capabilities theory. Subsequently, a focus group is conducted to refine our propositions, providing insights and examples demonstrating the potential value of blockchain in project management.FindingsThe findings suggest that blockchain significantly impacts the information quality within PMIS. This improvement in information quality enhances traceability, reliability and security of project data, facilitating better decision-making and governance. The focus group revealed blockchain’s benefits in managing confidential data and streamlining knowledge sharing processes, ultimately contributing to project portfolio success.Originality/valueThis research offers a novel conceptual framework and original insights into the application of blockchain in project management, particularly within the context of Industry 4.0, paving the way for future research on digital transformation in project management.

MARKETING RESEARCH OF ENVIRONMENTAL CULTURE POPULATION OF IZHEVSK

The article presents the results of a marketing study of the environmental culture and behavior of the population of the city of Izhevsk in 2023. The goals and objectives, the population under study, the size and structure of the sample, and the analysis tools are defined. An assessment of the actual sample obtained was given, on the basis of which a database was formed and an assessment of environmental culture and behavior was carried out. This study was initiated by the author of the article with the aim of testing a previously developed marketing model for assessing the level of environmental culture and behavior of the population “cognition-feeling-action”. The study became necessary to substantiate a number of provisions in the field of: environmental management and management against the backdrop of growing environmental problems in Russia and its regions; systematization of ideas about corporate social responsibility from the standpoint of justifying the rationality of its application; formation of marketing methodology for studying environmental culture and behavior of the population. Based on the results of a mass sample survey, a general and segment-by-segment assessment of the environmental culture and behavior of the population of Izhevsk in 2023 was given. The survey made it possible to record a number of indicators of the cognitive, affective and behavioral reaction of the population in the environmental sphere in general and by segment: “young” population (18 -44 years old); “middle-aged” population (45-59 years old), “elderly” people (over 60 years old). The structure of knowledge of environmental projects and programs, public and state bodies/institutions responsible for the state of the environment in the city of Izhevsk and Udmurtia has been studied. An assessment of the environmental situation in the city of Izhevsk by segment was recorded, and differentiation of people’s opinions regarding the consequences of environmental deterioration and the relevance of environmental problems was noted. An indirect study of the behavioral attitudes of a “responsible citizen” made it possible to assess the norms of behavior of the population and their readiness to act. Based on people's opinions, a rating of the relevance of measures to improve the environmental situation in the city of Izhevsk and the Urals was formed. As a result, an assessment of ecological culture is given by segments in the “cognition-feeling-action” model and illustrated graphically.

Issue on Teaching and Training Future Health Informaticians:Partnering with Students to Develop a Capstone for a Graduate Health Informatics Program.

To assess the desirability, feasibility, and sustainability of integrating a project-based capstone course with the course-based curriculum of an interdisciplinary MSc health informatics program guided with a student-partnered steering committee and student-centered approach. We conducted an online cross-sectional survey (n=87) and three semi-structured focus groups (n=18) of health informatics students and alumni. Survey data was analyzed descriptively. Focus groups were audio-recorded and transcribed verbatim and then analyzed using a general inductive and classic analysis approach. Most students were supportive of including a capstone project but desired an option to work independently or within a group. Students perceived several benefits to capstone courses while concerned over perceived challenges to capstone implementation, evaluation, and managing group processes. Themes identified were: 1) professional development, identity, and career advancement; 2) emulating the real world and learning beyond the classroom, 3) embracing new, full circle learning, 4) anticipated course structure, delivery, and preparation, 5) balancing student choice, interests, and priorities, and 6) concerns over group dynamics, limitations, and support. This study demonstrates the value of having students as partners at each stage in the process from methods conception to course curriculum design. With the steering committee and the curriculum developer, we codeveloped a student-centered course that integrates foundational digital health-related project knowledge acquisition with an inquiry-based project which can be completed independently or in small groups. This study demonstrates the potential benefits and challenges that health informatics educators may consider when (re)-designing capstone courses.

A Study on Usage of Formal Knowledge Sharing Systems in Project-Based Organizations

Projects are one-time activities that have time and resource limitations. Projects are considered as arenas for learning. There are opportunities for creating new knowledge and utilizing existing knowledge in projects. Since projects are per definition unique, there is at least some degree of uniqueness associated with each project. This uniqueness provides opportunities to learn something new in each project. The known element in projects – such as existing solutions that have been proven to produce good results – can be shared and utilized to avoid using time and resources to create the same or similar solutions from scratch. Knowledge sharing and learning can play a notable role in improving the ability of project-based organizations to carry out their projects successfully and contributing to obtain higher productivity, profitability and sustainable competitive advantage. However, there are several challenges. This paper looks at one of the challenges, namely ineffective usage of formal knowledge sharing systems in project-based organizations. In this regard, the following two research questions are considered: (1) What are the challenges of using formal knowledge sharing systems? (2) How can the challenges be addressed? Formal knowledge sharing systems play an important role in supporting knowledge sharing and learning in organizations. Technological development has created many possibilities to enhance functions as well as benefits of such systems. However, there are several factors that are to be taken into consideration in order to make sure an effective usage of formal knowledge sharing systems in project-based organizations. This paper presents six factors in this regard and emphasizes the need to have a strategic view and a top management perspective of knowledge sharing and learning in project-based organizations. The study on which this paper is based, is of qualitative nature. In this regard, interviews have been conducted to gather information from five public project-based organizations in Norway.

Bilateral connections from the amygdala to extrastriate visual cortex in the marmoset monkey.

It is known that the primate amygdala forms projections to many areas of the ipsilateral cortex, but the extent to which it forms connections with the contralateral visual cortex remains less understood. Based on retrograde tracer injections in marmoset monkeys, we report that the amygdala forms widespread projections to the ipsilateral extrastriate cortex, including V1 and areas in both the dorsal (MT, V4T, V3a, 19M, and PG/PFG) and the ventral (VLP and TEO) streams. In addition, contralateral projections were found to target each of the extrastriate areas, but not V1. In both hemispheres, the tracer-labeled neurons were exclusively located in the basolateral nuclear complex. The number of labeled neurons in the contralateral amygdala was small relative to the ipsilateral connection (1.2% to 5.8%). The percentage of contralateral connections increased progressively with hierarchical level. An injection in the corpus callosum demonstrated that at least some of the amygdalo-cortical connections cross through this fiber tract, in addition to the previously documented path through the anterior commissure. Our results expand knowledge of the amygdalofugal projections to the extrastriate cortex, while also revealing pathways through which visual stimuli conveying affective content can directly influence early stages of neural processing in the contralateral visual field.