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The factors for higher education institutions’ (HEIs) project management failure have been studied for several years. One of the issues is a lack of tools to combine their knowledge infrastructure capabilities (KIC) with project management (PM) to examine these infrastructures and monitor maturity. There are several project management maturity (PMM) models available. However, there are just a few empirical studies that support the three knowledge infrastructure capabilities and PMM integrations. As a result, the current research aims to suggest a new conceptual model, KIC-knowledge management (KM), and assess a research model that includes the three knowledge infrastructure capabilities as a prerequisite to elevate the PMM.

Partial least squares structural equation modeling (PLS-SEM) is used to evaluate the proposed research model. The study’s hypotheses were also examined using a sample of 352 respondents from PM departments at ten Yemeni public universities.

The study found that if the three key knowledge infrastructure capabilities integrate into the PMs, then it will help HEIs to perform project tasks more effectively and efficiently. Also, it will improve the PM maturity level if all the three capabilities positively effect PMM.

The study findings cannot be generalized to other industries because the collected date were with the Yemeni public universities’ context. Also, the new proposed model can be assessed in various sectors to increase the validity of the model. One more thing, future academics can conduct qualitative research study to validate again the proposed model.

Project managers can develop and improve their organization’s effectiveness and performance by focusing on these findings and using the developed model. Also, the findings of this study can be used as a benchmark for evaluating initiatives and knowledge-based governmental entities.

It is an opportunity for knowledge-based governmental entities particularly and other organizations to elevate most of projects to achieve a supreme level of maturity. Also, this study will assist employees to understand the relationship between KICs and projects within HEIs in Yemen.

This paper is among the first to empirically study the impact of the three knowledge infrastructure capabilities toward PMM. It links between two important domains: KM and PM.

Learning from safety accidents and sharing safety knowledge has become an important part of accident prevention and improving construction safety management. Considering the difficulty of reusing unstructured data in the construction industry, the knowledge in it is difficult to be used directly for safety analysis. The purpose of this paper is to explore the construction of construction safety knowledge representation model and safety accident graph through deep learning methods, extract construction safety knowledge entities through BERT-BiLSTM-CRF model and propose a data management model of data–knowledge–services.

The ontology model of knowledge representation of construction safety accidents is constructed by integrating entity relation and logic evolution. Then, the database of safety incidents in the architecture, engineering and construction (AEC) industry is established based on the collected construction safety incident reports and related dispute cases. The construction method of construction safety accident knowledge graph is studied, and the precision of BERT-BiLSTM-CRF algorithm in information extraction is verified through comparative experiments. Finally, a safety accident report is used as an example to construct the AEC domain construction safety accident knowledge graph (AEC-KG), which provides visual query knowledge service and verifies the operability of knowledge management.

The experimental results show that the combined BERT-BiLSTM-CRF algorithm has a precision of 84.52%, a recall of 92.35%, and an F1 value of 88.26% in named entity recognition from the AEC domain database. The construction safety knowledge representation model and safety incident knowledge graph realize knowledge visualization.

The proposed framework provides a new knowledge management approach to improve the safety management of practitioners and also enriches the application scenarios of knowledge graph. On the one hand, it innovatively proposes a data application method and knowledge management method of safety accident report that integrates entity relationship and matter evolution logic. On the other hand, the legal adjudication dimension is innovatively added to the knowledge graph in the construction safety field as the basis for the postincident disposal measures of safety accidents, which provides reference for safety managers' decision-making in all aspects.

Study on knowledge-based systems for scientific publications is growing very broadly. However, most of these studies do not explicitly discuss the knowledge management (KM) component as knowledge management system (KMS) implementation. This background causes academic institutions to face challenges in developing KMS to support scholarly publication cycle (SPC). Therefore, this study aims to develop a new KMS conceptual model, Identify critical components and provide research gap opportunities for future KM studies on SPC.

This study used a systematic literature review (SLR) method with the procedure from Kitchenham et al. Then, the SLR results are compiled into a conceptual model design based on a framework on KM foundations and KM solutions. Finally, the model design was validated through interviews with related field experts.

The KMS for SPC focuses on the discovery, sharing and application of knowledge. The majority of KMS use recommendation systems technology with content-based filtering and collaborative filtering personalization approaches. The characteristics data used in KMS for SPC are structured and unstructured. Metadata and article abstracts are considered sufficiently representative of the entire article content to be used as a search tool and can provide recommendations. The KMS model for SPC has layers of KM infrastructure, processes, systems, strategies, outputs and outcomes.

This study has limitations in discussing tacit knowledge. In contrast, tacit knowledge for SPC is essential for scientific publication performance. The tacit knowledge includes experience in searching, writing, submitting, publishing and disseminating scientific publications. Tacit knowledge plays a vital role in the development of knowledge sharing system (KSS) and KCS. Therefore, KSS and KCS for SPC are still very challenging to be researched in the future. KMS opportunities that might be developed further are lessons learned databases and interactive forums that capture tacit knowledge about SPC. Future work potential could identify other types of KMS in academia and focus more on SPC.

This study proposes a novel comprehensive KMS model to support scientific publication performance. This model has a critical path as a KMS implementation solution for SPC. This model proposes and recommends appropriate components for SPC requirements (KM processes, technology, methods/techniques and data). This study also proposes novel research gaps as KMS research opportunities for SPC in the future.

Generative AI and more specifically ChatGPT has brought a revolution in the lives  of people by providing them with required knowledge that it has learnt from an exponentially large knowledge base. In this viewpoint, we are initiating the debate and offer the first step towards Generative AI based knowledge management systems in organizations.

This study is a viewpoint and develops a conceptual foundation using existing literature on how ChatGPT can enhance the KM capability based on Nonaka’s SECI model. It further supports the concept by collecting data from a public sector univesity in Hong Kong to strenghten our argument of ChatGPT mediated knowledge management system.

We posit that all four processes, that is Socialization, Externalization, Combination and Internalization can significantly improve when integrated with ChatGPT. ChatGPT users are, in general, satisfied with the use of ChatGPT being capable of facilitating knowledge generation and flow in organizations.

The study provides a conceptual foundation to further the knowledge on how ChatGPT can be integrated within organizations to enhance the knowledge management capability of organizations. Further, it develops an understanding on how managers and executives can use ChatGPT for effective knowledge management through improving the four processes of Nonaka’s SECI model.

This is one of the earliest studies on the linkage of knowledge management with ChatGPT and lays a foundation for ChatGPT mediated knowledge management system in organizations.

This study aims to investigate the key factors that influence the behavioural intention of doctors to adopt the knowledge sharing driven blockchain technology in government hospitals. The study is based on the Unified Theory of Acceptance and Use of Technology 2, with the addition of trust as an independent variable and knowledge sharing as a mediating variable between trust and behavioural intention.

The data for the study was collected through a correlation and cross-sectional study using a survey, with a sample of 322 responses being used for the final analysis. The initial analysis of the data was conducted using SPSS v.26, followed by a partial least squares structural equation modelling (PLS-SEM) analysis using SmartPLS v.3.9 to test the validity and reliability of the measures and to examine the hypothesized relationships.

The results supported the proposed framework. The results of PLS-SEM indicate that all proposed pathways support the model. In particular, the results of the study reveal that performance expectation, effort expectation, social influence, facilitation conditions and trust are drivers of blockchain adoption and have a significant impact on the behavioural intention of clinicians in hospitals. Furthermore, the study found that knowledge sharing mediated the relationship between trust and behavioural intention.

The present study sheds light on the challenges facing blockchain technology, such as privacy and trust concerns and proposes a more sustainable approach based on knowledge management to enhance the effectiveness of blockchain technology and overcome these challenges.

The significance of this paper lies in the limited literature examining the relationships between blockchain technology and knowledge management processes. Furthermore, a hypothetical framework that includes the knowledge sharing process as a mediating variable between trust and behavioural intention to adopt blockchain technology has not been presented or developed in any previous studies, particularly in the context of Iraq. Thus, this work is novel and unique in its approach.

Effective management of risk and knowledge is critical to ensure the success of industry–university collaboration (IUC) projects. However, the intricate dynamics through which these factors influence the performance of IUC projects have yet to be fully investigated. The purpose of this study is to explore the interplay between risk management and knowledge management capabilities and their impact on IUC project performance.

A model was constructed and evaluated through the examination of a sample of 188 collaborative innovation projects located in the United Arab Emirates (UAE), utilizing structural equation models (SEM) and hierarchical regression analysis.

The findings indicate that social system risk, technical system risk and project management risk have a negative impact on the performance of university–industry collaboration (UIC) projects, while cultural, technical and structural knowledge management capabilities can mitigate the negative impact of these risks on the performance of IUC projects.

The study concludes with three recommendations aimed at improving the management of UIC projects, including the establishment of a distinct and precise management strategy, the deployment of a comprehensive and systematized management methodology and the adoption of a balanced management framework.

The originality and value of this study lie in its exploration of the interplay between risk management and knowledge management capabilities in IUC projects. While previous studies have examined either risk management or knowledge management in IUC projects separately, this study provides a comprehensive analysis of both factors and their combined impact on project performance. The study also contributes to the literature by highlighting the specific risks and knowledge management capabilities that are most relevant to the context of IUC projects in the UAE. The practical recommendations offered by the study can help project managers and stakeholders to improve the success of collaborative innovation projects.

The application of knowledge management (KM) in government agencies is one strategy to deal with government problems effectively and efficiently. This study aims to identify KM readiness critical success factors (CSFs), measure the level of readiness for KM implementation, identify improvement initiatives and develop KM readiness models for government agencies. This model plays a role in the implementation of KM successful.

The level of readiness is obtained by calculating the factor weights of the opinions of experts using the entropy method. The readiness value is calculated from the results of the questionnaire with average descriptive statistics. The method for analysis of improvement initiatives adopts the Asian Productivity Organization framework. The model was developed based on a systems approach and expert validation.

Reliability testing with a Cronbach’s alpha value for entropy is 0.861 and the questionnaire is 0.920. The result of measuring KM readiness in government agencies is 75.29% which is at level 3 (ready/needs improvement). The improvement in the level of readiness is divided into two parts: increasing the value of factors that are still less than ready (75%) and increasing the value of all factors to level 4 (84%). The model consists of three main sections: input (KMCSFs), process (KM readiness) and output (KM implementation).

The first suggestion is that the sample of employees used in this study is still in limited quantities, that is, 50% of the total population. The second limitation is determining KMCSFs. According to experts, combining this study with factor search and correlation computations would make it more complete. The expert’s advice aims to obtain factors that can be truly tested both subjectively and objectively. Finally, regarding literature selection for future research, it is recommended to use a systematic literature review such as the preferred reporting items for systematic reviews and meta-analyses and Kitchenham procedures.

The management must also prioritize KMCSF according to its level and make KMCSF a key performance indicator. For example, at the priority level, active leadership in KM is the leading performance indicator of a leader. Then at the second priority level, management can make a culture of sharing an indicator of employee performance through a gamification program. The last point that management must pay attention to in implementing all of these recommendations is to collaborate with relevant stakeholders, for example, those authorized to draft regulations and develop human resources.

This study proposes a novel comprehensive framework to measure and improve KM implementation readiness in government agencies. This study also proposes a KMCSF and novel KM readiness model with its improvement initiatives through this framework.

This study aims to extend and explore patterns and trends of research in the linkage of big data and knowledge management (KM) by identifying growth in terms of numbers of papers and current and emerging themes and to propose areas of future research.

The study was conducted by systematically extracting, analysing and synthesizing the literature related to linkage between big data and KM published in top-tier journals in Web of Science (WOS) and Scopus databases by exploiting bibliometric techniques along with theory, context, characteristics, methodology (TCCM) analysis.

The study unfolds four major themes of linkage between big data and KM research, namely (1) conceptual understanding of big data as an enabler for KM, (2) big data–based models and frameworks for KM, (3) big data as a predictor variable in KM context and (4) big data applications and capabilities. It also highlights TCCM of big data and KM research through which it integrates a few previously reported themes and suggests some new themes.

This study extends advances in the previous reviews by adding a new time line, identifying new themes and helping in the understanding of complex and emerging field of linkage between big data and KM. The study outlines a holistic view of the research area and suggests future directions for flourishing in this research area.

This study highlights the role of big data in KM context resulting in enhancement of organizational performance and efficiency. A summary of existing literature and future avenues in this direction will help, guide and motivate managers to think beyond traditional data and incorporate big data into organizational knowledge infrastructure in order to get competitive advantage.

To the best of authors’ knowledge, the present study is the first study to go deeper into understanding of big data and KM research using bibliometric and TCCM analysis and thus adds a new theoretical perspective to existing literature.

Organizations widely adopt knowledge management (KM) to develop and promote technologies and improve business effectiveness. Analytics can aid in KM, further augmenting company performance and decision-making. There has been significant research in the domain of analytics in KM in the past decade. Therefore, this paper aims to examine the current body of literature on the adoption of analytics in KM by offering prominent themes and laying out a research path for future research endeavors in the field of KM analytics.

A comprehensive analysis was conducted on a collection of 123 articles sourced from the Scopus database. The research has used a Latent Dirichlet Allocation methodology for topic modeling and content analysis to discover prominent themes in the literature.

The KM analytics literature is categorized into three clusters of research – KM analytics for optimizing business processes, KM analytics in the industrial context and KM analytics and social media.

Systematizing the literature on KM and analytics has received very minimal attention. The KM analytics view has been examined using complementary topic modeling techniques, including machine-based algorithms, to enable a more reliable, systematic, thorough and objective mapping of this developing field of research.

The ongoing paradigm shift in the energy sector holds paramount implications for the realization of the sustainable development goals, encompassing critical domains such as resource optimization, environmental stewardship and workforce opportunities. Concurrently, this transformative trajectory within the power sector possesses a dual-edged nature; it may ameliorate certain challenges while accentuating others. In light of the burgeoning research stream on open innovation, this study aims to examine the intricate dynamics of knowledge-based industry-university-research networking, with an overarching objective to elucidate and calibrate the equilibrium of ambidextrous innovation within power systems.

The authors scrutinize the role of different innovation organizations in three innovation models: ambidextrous, exploitative and exploratory, and use a multiobjective decision analysis method-entropy weight TOPSIS. The research was conducted within the sphere of the power industry, and the authors mined data from the widely used PatSnap database.

Results show that the breadth of knowledge search and the strength of an organization’s direct relationships are crucial for ambidextrous innovation, with research institutions having the highest impact. In contrast, for exploitative innovation, depth of knowledge search, the number of R&D patents and the number of innovative products are paramount, with universities playing the most significant role. For exploratory innovation, the depth of knowledge search and the quality of two-mode network relations are vital, with research institutions yielding the best effect. Regional analysis reveals Beijing as the primary hub for ambidextrous and exploratory innovation organizations, while Jiangsu leads for exploitative innovation.

The study offers valuable implications to cope with the dynamic state of ambidextrous innovation performance of the entire power system. In light of the findings, the dynamic state of ambidextrous innovation performance within the power system can be adeptly managed. By emphasizing a balance between exploratory and exploitative strategies, stakeholders are better positioned to respond to evolving challenges and opportunities. Thus, the study offers pivotal guidance to ensure sustained adaptability and growth in the power sector’s innovation landscape.

The primary originality is to extend and refine the theoretical understanding of ambidextrous innovation within power systems. By integrating several theoretical frameworks, including social network theory, knowledge-based theory and resource-based theory, the authors enrich the theoretical landscape of power system ambidextrous innovation. Also, this inclusive examination of two-mode network structures, including the interplay between knowledge and cooperation networks, unveils the intricate interdependencies between these networks and the ambidextrous innovation of power systems. This approach significantly widens the theoretical parameters of innovation network research.