With the rapid development of Artificial Intelligence (AI) technology, significant breakthroughs have been made in its application in many fields. Especially, in the field of enterprise human resource management and employee performance evaluation, AI has demonstrated its powerful ability to optimise and improve performance. This study explores the application of AI in enterprise human resource management and how to use AI to evaluate employee performance. The research includes analysing and comparing existing AI-driven human resource management models, evaluating how AI can help improve employee performance and leadership styles, and designing and developing human resource management computer systems for enterprise employees. Through empirical research and case analysis, this study proposes a new AI-optimised employee performance evaluation model and explores its application and effect in practice. In general, the application of AI can improve the efficiency and accuracy of enterprise human resource management, and provide new possibilities for employee performance evaluation. At present, artificial intelligence technology has been widely used in various fields of daily life, especially in corporate human resource management, providing better support for the development of enterprises.

In order to improve the quality of higher education, student satisfaction, and employment rate, a data mining based high-quality management method for higher education is proposed. Firstly, construct a high-quality evaluation system for higher education based on the principles of education quality evaluation. Secondly, the association rule mining method is used to construct a university education quality management model and determine the weight of the impact indicators for high-quality management of university education. Finally, the fuzzy evaluation method is used to determine the high-quality evaluation function of higher education, and the results of high-quality evaluation of higher education are obtained. High-quality management strategies are developed based on the evaluation results to improve the quality of education. The experimental results show that the student satisfaction rate of this method can reach 99.3%, and the student employment rate can reach 99.9%.

Presently, there are many existing academic studies on the development, protection and operation of intellectual property management (IPM). Therefore, provides a comprehensive econometric analysis in order to provide scholars, with a clearer understanding of the evolution and development of IP management research during 2000 to 2022. The study is aiming to help scholars to better discern the expanding IPM research field from a multidimensional perspective. The database used for this analysis is the Web of Science Core Collection database. After retrieval through keywords and using a variety of tools such as CiteSpace, VOSviewer, Bibliometrix and HistCite, 1033 documents were refined to conduct the econometric analysis, and produce graphs. The findings indicate that the US is a highly active country/region in the field IP management research, and its expanding IP management research is branching out into other disciplines. The study also presents the future directions and possible challenges for IPM in technology management.

The European Union Rights Management Directive 2014/26/EU, provides regulatory oversight of European Union (EU) Collective Management Organizations (CMOs). However, the Directive has no provision indicating how members of EU CMOs may conduct non-financial audits of their CMO income and reporting. This paper addresses the problem of a lack of an audit method through a case study of the five writer members of the music group Duran Duran, who have been members of the UK's CMO for performing rights - the Performing Rights Society (PRS) for over 35 years. The paper argues a new audit CMO member method that can address the lacunae regarding the absence of CMO member right to audit a CMO and an applicable CMO audit method.

As academia in general, and research funders in particular, place ever greater importance on data as an output of research, so the value of good research data management practices becomes ever more apparent. In response to this, the Innovative Design and Manufacturing Research Centre (IdMRC) at the University of Bath, UK, with funding from the JISC, ran a project to draw up a data management planning regime. In carrying out this task, the ERIM (Engineering Research Information Management) Project devised a visual method of mapping out the data records produced in the course of research, along with the associations between them. This method, called Research Activity Information Development (RAID) Modelling, is based on the Unified Modelling Language (UML) for portability. It is offered to the wider research community as an intuitive way for researchers both to keep track of their own data and to communicate this understanding to others who may wish to validate the findings or re-use the data.

Big data management is a reality for an increasing number of organizations in many areas and represents a set of challenges involving big data modeling, storage and retrieval, analysis and visualization. However, technological resources, people and processes are crucial to facilitate the management of big data in any kind of organization, allowing information and knowledge from a large volume of data to support decision-making. Big data management can be supported by these three dimensions: technology, people and processes. Hence, this article discusses these dimensions: the technological dimension that is related to storage, analytics and visualization of big data; the human aspects of big data; and, in addition, the process management dimension that involves in a technological and business approach the aspects of big data management.

Aim/Purpose: The proposed Personal Knowledge Management (PKM) for Empowerment (PKM4E) Framework expands on the notions of the Ignorance Map and Matrix to support the educational and informing concept of a PKM system-in-progress. Background: The accelerating information abundance is depleting the very attention our cognitive capabilities are able to master, contributing to widening individual and collective opportunity divides. Support is urgently needed to benefit Knowledge Workers irrespective of space (developed/developing countries), time (study or career phase), discipline (natural or social science), or role (student, professional, leader). Methodology: The Design Science Research (DSR) project conceptualizing the PKM System (PKMS) aims to support a scenario of a ‘Decentralizing KM Revolution’ giving more power and autonomy to individuals and self-organized groups. Contribution: The informing-science-related approach synthesizes and visualizes concepts related to ignorance and entropy, learning and innovation, chance discovery and abduction to inform diverse audiences and potential beneficiaries. Findings: see Recommendation for Researchers Recommendations for Practitioners: The PKM4E learning cycles and workflows apply ‘cumulative synthesis’, a concept which convincingly couples the activities of researchers and entrepreneurs and assists users to advance their capability endowments via applied learning. Recommendation for Researchers: In substituting document-centric with meme-based knowledge bases, the PKMS approach merges distinctive voluntarily shared knowledge objects/assets of diverse disciplines into a single unified digital knowledge repository and provides the means for advancing current metrics and reputation systems. Impact on Society: The PKMS features provide the means to tackle the widening opportunity divides by affording knowledge workers with continuous life-long support from trainee, student, novice, or mentee towards professional, expert, mentor, or leader. Future Research: After completing the test phase of the PKMS prototype, its transformation into a viable PKM system and cloud-based server based on a rapid development platform and a noSQL-database is estimated to take 12 months.

Aim/Purpose: The need for Higher Education Institutions (HEIs) to maximize the use of their intellectual property and strategic resources for research and teaching has become ever more evident in recent years. Furthermore, little attention is paid in developing an enabling system that will facilitate knowledge transfer in the Research-Teaching Nexus (RTN). Hence, this study assesses the current state of practice in knowledge management of the nexus in higher education in Oman. It also explores the context of how Knowledge Management System (KMS) for the nexus can be designed and utilized by HEIs and challenges them to rethink their traditional approaches in managing their knowledge as-sets to boost individual and organizational learning. Background: This study provides a Knowledge Management-based framework and design of a knowledge management system that support the academic community towards the improvement of the nexus. This study sets out ideas from various academic and professional experts on how academic stakeholders in the higher education can improve and promote knowledge transfer and make better use of its knowledge and research assets for teaching and learning. It stressed the importance of having the knowledge assets or resources that can easily be pooled, accessed, and made available to its intended stakeholders. Methodology: Data were gathered from 29 out of 49 institutional quality audit reports of all HEIs in Oman. The panel comments were coded and analysed to extract valuable insights regarding the management of knowledge assets in research. Additionally, data were gathered from the institutional accreditation outcomes page of the same website. Manifest and latent content analyses were used in reporting the findings of the panel. Contribution: The study will contribute to a greater understanding and acceptance of Knowledge Management (KM) in higher education and extended the body of knowledge concerning knowledge management for the RTN. Findings: The reports revealed a very limited practice of the nexus in terms of people and culture, structure ad processes, and computing and web technologies. A few staff are involved in RTN work, there is an uneven understanding of the RTN among staff, limited joint research between staff and students are some of the reasons for this. Significantly, there is no explicit research framework or policy for the RTN, and systems and/or mechanisms are limited. Further-more, the reports did not account any use of computing and web technologies for the nexus. These limitations can lead to students with less academic, research, and graduate skills. Hence, this study presents a feature design of a KMS that incorporates various RTN best practices, as informed by the reports and literature. The design will allow the staff to utilize the research assets in the classroom, at the same time, engages students in research and scholarly under-takings. Recommendations for Practitioners: All HEIs must have a innovative system that integrates a formal agenda and approach, and set initiatives, strategies, policies, and procedures for knowledge management in utilizing research assets for teaching and learning. It must be designed so that RTN practices remain up-to-date, relevant, and responsive to the needs of the stakeholders, as well as, address academic accreditation challenges. Recommendation for Researchers: Researchers can evaluate the knowledge management of RTN practices of other HEIs outside of Oman to effectively recommend the proper course of action for teaching and learning improvement. Impact on Society: This study will redefine the role and contribution of HEIs, which are key players in advancing a knowledge economy. HEIs are expected to be powerhouses where academic knowledge is discovered, created, disseminated, shared, and re-invented. They must be able to fully grasp the value of managing knowledge to be able to effect positive and purposeful change to the community. Future Research: Future work should include staff and student surveys that examine the knowledge management need of the learning organization to better inform the design of a KMS for the RTN. Thereafter, future research can test the stage to test the effectiveness of the conceptual design.

Aim/Purpose: Board of Directors seek to use their big data as a competitive advantage. Still, scholars note the complexities of corporate governance in practice related to information security risk management (ISRM) effectiveness. Background: While the interest in ISRM and its relationship to organizational success has grown, the scholarly literature is unclear about the effects of Chief Technology Officers (CTOs) leadership styles, the alignment of the governance of big data, and ISRM effectiveness in organizations in the West-ern United States. Methodology: The research method selected for this study was a quantitative, correlational research design. Data from 139 participant survey responses from Chief Technology Officers (CTOs) in the Western United States were analyzed using 3 regression models to test for mediation following Baron and Kenny’s methodology. Contribution: Previous scholarship has established the importance of leadership styles, big data governance, and ISRM effectiveness, but not in a combined understanding of the relationship between all three variables. The researchers’ primary objective was to contribute valuable knowledge to the practical field of computer science by empirically validating the relationships between the CTOs leadership styles, the alignment of the governance of big data, and ISRM effectiveness. Findings: The results of the first regression model between CTOs leadership styles and ISRM effectiveness were statistically significant. The second regression model results between CTOs leadership styles and the alignment of the governance of big data were not statistically significant. The results of the third regression model between CTOs leadership styles, the alignment of the governance of big data, and ISRM effectiveness were statistically significant. The alignment of the governance of big data was a significant predictor in the model. At the same time, the predictive strength of all 3 CTOs leadership styles was diminished between the first regression model and the third regression model. The regression models indicated that the alignment of the governance of big data was a partial mediator of the relationship between CTOs leadership styles and ISRM effectiveness. Recommendations for Practitioners: With big data growing at an exponential rate, this research may be useful in helping other practitioners think about how to test mediation with other interconnected variables related to the alignment of the governance of big data. Overall, the alignment of governance of big data being a partial mediator of the relationship between CTOs leadership styles and ISRM effectiveness suggests the significant role that the alignment of the governance of big data plays within an organization. Recommendations for Researchers: While this exact study has not been previously conducted with these three variables with CTOs in the Western United States, overall, these results are in agreement with the literature that information security governance does not significantly mediate the relationship between IT leadership styles and ISRM. However, some of the overall findings did vary from the literature, including the predictive relationship between transactional leadership and ISRM effectiveness. With the finding of partial mediation indicated in this study, this also suggests that the alignment of the governance of big data provides a partial intervention between CTOs leadership styles and ISRM effectiveness. Impact on Society: Big data breaches are increasing year after year, exposing sensitive information that can lead to harm to citizens. This study supports the broader scholarly consensus that to achieve ISRM effectiveness, better alignment of governance policies is essential. This research highlights the importance of higher-level governance as it relates to ISRM effectiveness, implying that ineffective governance could negatively impact both leadership and ISRM effectiveness, which could potentially cause reputational harm. Future Research: This study raised questions about CTO leadership styles, the specific governance structures involved related to the alignment of big data and ISRM effectiveness. While the research around these variables independently is mature, there is an overall lack of mediation studies as it relates to the impact of the alignment of the governance of big data. With the lack of alignment around a universal framework, evolving frameworks could be tested in future research to see if similar results are obtained.