Knowledge Innovation Research Articles

RETRACTED ARTICLE: The best configuration of collaborative knowledge innovation management from the perspective of artificial intelligence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

Sea cucumbers and their symbiotic microbiome have evolved to feed on seabed sediments

Sea cucumbers are predominant deposit feeders in benthic ecosystems, providing protective benefits to coral reefs by reducing disease prevalence. However, how they receive sufficient nutrition from seabed sediments remains poorly understood. Here, we investigate Holothuria leucospilota, an ecologically significant tropical sea cucumber, to elucidate digestive mechanisms underlying marine deposit-feeding. Genomic analysis reveals intriguing evolutionary adaptation characterized by an expansion of digestive carbohydrase genes and a contraction of digestive protease genes, suggesting specialization in digesting microalgae. Developmentally, two pivotal dietary shifts, namely, from endogenous nutrition to planktonic feeding, and from planktonic feeding to deposit feeding, induce changes in digestive tract enzyme profiles, with adults mainly expressing carbohydrases and lipases. A nuanced symbiotic relationship exists between gut microbiota and the host, namely, specific resident bacteria supply crucial enzymes for food digestion, while other bacteria are digested and provide assimilable nutrients. Our study further identifies Holothuroidea lineage-specific lysozymes that are restrictedly expressed in the intestines to support bacterial digestion. Overall, this work advances our knowledge of the evolutionary innovations in the sea cucumber digestive system which enable them to efficiently utilize nutrients from seabed sediments and promote food recycling within marine ecosystems.

Gender differences in the patterns and consequences of changing research directions in scientific careers

Abstract Changes of research direction in scientific careers are related to the so-called “essential tension” between the exploration of new knowledge and the exploitation of established knowledge in research and innovation. Changes of research direction are thereby assumed to influence the evolution of science in general. Research has shown that such changes may also affect the success of individual scientists in their careers. However, the gender dimension of this aspect of career development is so far understudied. There is also a need for more dynamic indicators to record and interpret career developments in macro data. This study combines the gender perspective with the introduction of new indicators. We selected more than 29,000 scientists in Physics & Astronomy and studied them over six decades using a bibliographic data set from Scopus. We find that women are less likely to change research direction than their men counterparts, and that the research performance of women is less negatively affected by changing research direction. We discuss the policy implications of these findings as well as the methodological advancement related to the new indicators of career development.

Harnessing the Experience of Research and Indigenous Knowledge for Sustainable Agricultural Transformation in Arunachal Pradesh, India

Traditional knowledge includes methods for nutrient management that rely on natural resources and minimize reliance on artificial inputs. Techniques such as using botanical extracts like Bilb Rasyan and Gaajar Ghaas Svaras, as well as incorporating organic materials like cow urine and plant residues, help enrich the soil with essential nutrients. Farmers in various regions have implemented ITK practices for soil conservation. For instance, the use of Vetiver grass as a vegetative barrier helps prevent soil erosion and stabilizes slopes. This method is cost-effective and adaptable to different environmental conditions. Advocating for policies that recognize and support the integration of ITKs into mainstream agricultural research and extension services is crucial. The Protection of Plant Varieties and Farmers' Rights Act (PPVFR Act) in India provides legal recognition and protection for traditional knowledge and farmer innovations. The synthesis of Indigenous Technical Knowledge and modern agricultural research not only supports the livelihoods of local farmers but also contributes to environmental health and cultural preservation. This approach holds the promise of a more sustainable and prosperous future for Arunachal Pradesh and can serve as an inspiring model for other regions seeking to harmonize tradition with innovation.

The impact of knowledge management and innovation capacity on business results at FDI enterprises in Vietnam

The impact of knowledge management and innovation capacity on business results at FDI enterprises in Vietnam

Internal University Leading Function in Promoting Knowledge Economy in Tanzania

In response to the evolving demands of the knowledge economy (KE), Tanzanian universities are transforming their internal management practices to enhance effectiveness, competitiveness, and relevance. The study focuses on how universities manage activities that support knowledge creation, dissemination, and innovation. Data from 30 university leaders, gathered through document reviews and interviews, were thematically analysed. Findings reveal that leading include dynamism, empowerment, vision, and transparency motivate researchers to produce impactful academic research work with commercialization potential. However, the heavy reliance on institutional policies pushing rapid publication for promotion results in rushed, low-quality studies. The study emphasizes the need for leadership that goes beyond policy enforcement, fostering research with commercial value by leveraging personal leading qualities and providing adequate resources like grants and training

Game-based Learning for Science Education in Institutional Care Settings in Northwestern Mexico

Play has been key to knowledge acquisition, creativity, and innovation. In education, game-based learning (GBL) is a physical and digital environment that enhances learning outcomes in students. This paper presents the results of observational research on game-based learning in science education as a motivation for young students from two institutional care settings in Northwestern Mexico, in the State of Sonora. The observational research touched on 1) game-based learning in science, technology, engineering, and mathematics (STEM) education, and 2) learning motivation in vulnerable student groups. This paper focuses on the authors' experiences, most of them early researchers focused on science education, their purpose of promoting game-based learning in STEM education, and interviews with representatives from both institutions. We found that game-based learning was seen as a novelty, innovative, and motivational way of engaging young students from institutional care settings to learn and become curious about the STEM field. We also designed and created games focused on STEM subjects in both institutions. Students living in institutional care settings face even greater obstacles regarding education. In some instances, the lack of mentorship or follow-up tutoring activities can determine their academic outcome or lack thereof. With the ever-changing world of technology, industries demand highly qualified people in STEM fields. Nonetheless, international organizations such as the United Nations Educational, Scientific and Cultural Organization (UNESCO) and the Organisation for Economic Co-operation and Development (OECD) have reported that school systems around the world are not equipped with the proper teaching methodologies for students to acquire basic skills such as mathematics nor critical thinking. In Mexico, public school systems and institutional care settings often lack teacher training or mentorship approaches to promote STEM education. This paper concludes with a reflection on the benefits of GBL in STEM education in such settings and suggestions for student-led collaborations with children and youth in such settings.

How Individual Factors Shape Innovative Work Behavior in Malaysian HEIs: The Role of Innovation Capability, Knowledge Sharing and Psychological Empowerment

This study investigates the factors driving academics' innovative work behavior (IWB) in Malaysian Higher Education Institutions (HEIs), focusing on individual innovation capability, psychological empowerment, and knowledge sharing. The respondents comprised 382 academics from 24 Malaysian universities listed in the QS World Ranking 2023, selected through convenience sampling. Data were collected via a Google Form questionnaire distributed by email. The descriptive analysis assessed individual innovation capabilities, psychological empowerment, and knowledge-sharing levels, and their impact on IWB. The findings indicate high engagement in innovative activities, with academics demonstrating strong innovative capabilities, a sense of empowerment, and active knowledge-sharing practices. These individual factors significantly contribute to fostering IWB, highlighting the importance of continuous professional development, supportive institutional environments, and collaborative cultures in promoting innovation within HEIs. This study provides valuable insights for policymakers and university administrators to enhance the innovative potential of academics, ultimately contributing to the advancement of knowledge and improvement of educational practices in Malaysian HEIs. Future research should explore longitudinal effects and potential moderating factors to further understand innovative work behavior dynamics in academic settings.

From Anatomical Theatres to the Stage: Ferdinand and Dissection in the Duchess of Malfi

Unlike our 21st-century approaches to the disciplines, science and art were inextricably intertwined during the Early Modern Period. Innovations in scientific knowledge were both propagated and critiqued through various literary forms. Operating within the intersection of science and literature, this essay examines John Webster’s 17th-century play, The Duchess of Malfi, as a vehicle for critiquing contemporary anatomical dissections. Building upon Daisy Murray’s Twins in Early Modern English Drama and Shakespeare, and Albert H. Tricomi’s “The Severed Hand in Webster’s Duchess of Malfi,” this essay highlights the ways in which Webster engages with the anatomical dissections of his time. “From Anatomical Theatres to the Stage” begins with an overview of the history of European anatomical theatres and moves into an analysis of twinship as a dissection at birth and its implications for the early modern reader. Then, it considers Ferdinand’s association to witchcraft and his embodiment of the early modern surgeon as a means to showcase the immoral nature of the performance of human dissection. Beyond its demonstration of Webster’s engagement with his contemporary science, this essay ultimately illustrates the inseparability of art and science.

How do knowledge-intensive business services improve innovation? A resource-based model for antecedents of innovation in a developing country

Purpose Firms strive to find new ways of boosting value creation through integrating knowledge for innovation. Open innovation has altered this quest for excellence and, accordingly, this study aims to investigate the antecedents and enablers of innovation performance in firms belonging to a knowledge-intensive service sector of a developing country. Design/methodology/approach As such, data were gathered through a survey approach, targeting telecom service firms in Pakistan. Hypotheses were tested using the partial least squares technique. Measurement and structural models were assessed following the recommended two-stage procedure. Findings Results showed that knowledge sourcing and customer engagement impact the innovation performance of firms, whereas transformational leadership, knowledge sourcing and customer engagement are linked to innovation with the mediating effect of absorptive capacity. Practical implications This research helps to improve the innovation management practices of the firms belonging to this important sector and thus enables them to achieve sustainable competitive advantage through building their absorptive capabilities. Originality/value Knowledge-intensive business services are largely excluded from the great debate on open innovation so far, with a few exceptions. This research proposes and tests a model of enablers of open innovation that contributes toward improving the innovation performance of telecom firms in a developing country.

A STUDY ON THE IMPACT OF KNOWLEDGE HETEROGENITY ON TEAM CREATIVITY

This study categorizes the knowledge heterogeneity of "Internet+" college student innovation and entrepreneurship teams into three dimensions: explicit, pseudo-hidden, and true hidden. It examines their impact on team creativity by introducing team identification as a moderating variable. We construct a theoretical model from the perspective of team collaboration, drawing on knowledge innovation theory and social identity theory. We conducted the empirical study using SPSS and AMOS software, based on questionnaire data from 136 "Internet+" college student innovation and entrepreneurship competition teams. The results show that explicit and pseudo-hidden knowledge heterogeneity has significant positive effects on team creativity, whereas true hidden knowledge heterogeneity has a negative impact. Furthermore, team identification plays different moderating roles in the relationships between pseudo-hidden, true hidden knowledge heterogeneity, and team creativity.

Exploration of the Three in One Professional Course Ideological and Political Teaching Model of Virtue, Knowledge, and Creativity -- Taking the statistics major course Probability Theory as an example

Professional courses are important carriers of ideological and political education in courses. Probability Theory, as a core course in statistics, is of great significance in cultivating students ability to think randomly and solve complex problems. The existing ideological and political research on the course of Probability Theory mainly focuses on public basic courses. In depth analysis of the characteristics and curriculum features of statistics major, propose a deep integration of morality, knowledge, and creativity in the curriculum ideological and political education model, including the formation of morality, knowledge, and creativity integrated curriculum ideological and political education goals, the improvement of morality, knowledge, and creativity integrated curriculum ideological and political teaching links, the deepening of morality, knowledge, and creativity integrated curriculum ideological and political teaching content, and the innovation of morality, knowledge, and creativity integrated curriculum ideological and political teaching methods. Practice has shown that the three in one curriculum ideological and political model of morality, knowledge, and creativity effectively stimulates students interest in learning and enhances their professional confidence.

Evaluation of digital innovation in smart homes – based on bibliometrics and rooted theory

PurposeTo evaluate the level of digital innovation in the smart home industry.Design/methodology/approachBy employing bibliometric analysis and grounded theory, this study aims to assess the innovation capabilities of leading manufacturing enterprises and establish a digital innovation evaluation system for smart homes.FindingsScientific arguments show that the cultivation of innovation capability should focus on three aspects of knowledge innovation, product innovation and technological innovation. Utilizing the four first-level indicators and 15 second-level indicators derived from the previous sections, we conducted TF-IDF (Term Frequency-Inverse Document Frequency) words frequency matching on the annual reports of A-share smart home listed companies. The resulting score for the company across these four primary indicators was then considered as the score for the digital innovation level indicator.Originality/valueThe evaluation system includes first-level indicators such as technological innovation, production innovation, management innovation and sales innovation and second-level indicators such as digital technology, intelligent manufacturing, industrial Internet, green intelligent manufacturing, flexible robot production and cross-border integration, which provides smart home enterprises with a reference to understand the level of digital innovation, improve the deficiencies and promote the development of the smart home industry.

Embracing the role of business performance to foster the association between digital marketing, intellectual capital, and business sustainability

This study aims to explore the relationship between digital marketing and business performance and sustainability in small-medium enterprises (SMEs) in Bandung, Indonesia. Additionally, it examines the mediation power of business performance in enhancing the association of digital marketing, intellectual capital, and business sustainability. We employed a quantitative approach and acquired data from 308 SMEs in Bandung, Indonesia. Data collection utilized Google Forms and purposive sampling methods. We tested the hypotheses using Smart PLS. The results indicate that digital marketing has no significant effect on business performance. However, we found that intellectual capital acts as a catalyst for both business performance and sustainability. Given the identified role of intellectual capital as a catalyst for both business performance and sustainability, SMEs owners should prioritize investments in knowledge creation by providing employee training, fostering a culture of continuous learning, and supporting research and development initiatives. Investments in knowledge creation and innovation are crucial for SMEs to enhance business performance and sustainability. This involves providing employee training, fostering a culture of continuous learning, and supporting research and development initiatives.