Knowledge

This study examined how human resource management (HRM) training and development practices contribute to strengthening knowledge absorptive and protective capacities within South African state-owned enterprises (SOEs). Using an exploratory mixed-methods approach, this research was conducted in two phases. Firstly, 20 HR managers were interviewed, and annual reports from nine SOEs were reviewed. Thematic analysis, supported by Atlas.ti, revealed key insights that informed the design of a survey used in the second phase. In the second phase, the survey was administered to 585 randomly selected employees across three SOEs, achieving a 25% response rate. Data analysis carried out with Statistical Analysis Software (SAS) version 8.4 showed strong reliability, with a Cronbach’s alpha of 0.94. Findings indicate that training and development initiatives play a significant role in building absorptive capacity as they enhance knowledge acquisition and the ability to integrate new skills. These practices also reinforced employees’ tacit knowledge base, particularly through job-specific training and skills development. However, whilst HRM practices were effective in knowledge absorption, they were less successful in safeguarding and protecting critical tacit knowledge against potential loss. This study highlights the dual challenge facing SOEs: advancing employees’ capacity to absorb knowledge whilst also developing stronger mechanisms to protect valuable expertise. Full article

This paper examines the intersection of entrepreneurship, innovation, and sustainability in the tourism sector through the lens of knowledge creation and transfer. It focuses on the Tourism Creative Factory (TCF) ideation programme, developed under Turismo de Portugal’s Fostering Innovation in Tourism 2.0 initiative. Using a case study methodology, the research situates the 2021–2022 “RESTART” edition of TCF within broader theoretical frameworks of knowledge-based development and organisational learning. This study highlights the programme’s role in facilitating knowledge exchange among participants, mentors, and institutional actors, thereby enhancing entrepreneurial readiness and resilience in a post-pandemic context. Emphasis is placed on mentorship, capacity-building, and experiential learning as mechanisms for knowledge management, enabling the 39 selected participants to develop sustainable business models and Minimum Viable Products (MVPs), with the 16 most innovative being selected for a final pitch presentation to a panel of experts representing diverse sectors of the entrepreneurial ecosystem. The findings underscore the transferability of TCF’s methodology to other knowledge-intensive sectors and contribute to advancing theoretical and practical understanding of how structured ideation programmes function as knowledge systems within tourism and beyond. Full article

This study examines the factors driving perceived Study Efficiency and Exam Readiness associated with ChatGPT use among STEM students in higher education. Although prior research on generative artificial intelligence (GenAI) has largely focused on adoption and attitudes using descriptive or linear statistical approaches, limited empirical work has explored how students’ interactions with such tools relate to learning-related outcomes. To address this gap, this study applies an interpretable machine learning (ML) framework to identify key predictors of learning gains from ChatGPT use. Data were obtained from a large-scale global survey of STEM students (n = 10,525) across 109 countries and territories, capturing usage patterns, perceived capabilities, satisfaction, and academic outcomes. Two eXtreme Gradient Boosting (XGBoost)-based ML classification models were developed to predict Study Efficiency and Exam Readiness, and SHapley Additive exPlanations (SHAP) were used to interpret feature-level contributions. The models achieved strong predictive performance for the high-gain class, with an accuracy of 0.93 (F1 = 0.96) for Study Efficiency and 0.86 (F1 = 0.92) for Exam Readiness. Results indicate that motivation, personalized learning support, improved access to knowledge, facilitation of study activities, and exam-focused study assistance are key predictors of learning gains. These findings offer empirical and practical insights for educators and policymakers seeking to design effective and pedagogically sound AI-assisted learning environments in STEM education. Full article

The use of Artificial Intelligence (AI) in learning is expanding globally; however, the full potential of AI tools in the Open and Distance Learning (ODL) context, particularly at the Institute of Adult Education (IAE), remains underexplored. This study examined the IAE ODL students’ perspectives on the use of AI tools in learning. Specifically, it investigated ODL students’ familiarity with AI, AI preferences and use in learning, and perspectives on AI tool use in ODL. The study employed a mixed-methods approach, utilising a convergent parallel design to collect data from 93 second- and third-year ODL students at the Dar es Salaam and Morogoro Campuses. The findings revealed that 94.7% of students were familiar with AI, mainly after beginning their studies; 87% used ChatGPT for learning, and 57% used AI to answer their questions. In addition, 98% of students argued that the utilisation of AI in ODL is inevitable, citing its role in enhancing self-learning, improving access to learning materials, and saving time. Based on the findings, the study suggests that enhanced access to and awareness of diverse AI tools may help maximise their potential benefits in learning. The study also calls for academic integrity, ethical use, peer learning, and human-AI interaction among ODL students and institutions for the effective utilisation of AI in ODL. Full article

Complexity science studies systems in which properties and behaviors emerge at meso- and macroscales that are difficult to predict and model by observing the properties and behaviors exhibited by the system’s components at smaller scales. The set of relationships that exist among post-secondary school curricula and job markets is one example of such a system. Prior work has undertaken the challenge of modeling this system for several purposes, one of which has been to develop retrieval and ranking algorithms in the education–career domain. A particular emergent property that is closely bound up with this prior work, and that is the focus of the present work, is the salience of a course with respect to a specific objective. The specific objective that we are interested in here is career usefulness, which means that our overall task is to rank order courses based on their usefulness in helping a student to obtain and perform a specific job. One aspect of this overall task that remains understudied concerns how it can best be performed in an interpretable manner and whether existing interpretable methods that may be applied to it, such as text-based similarity measures and document-ranking functions, represent workable solutions or whether an approach involving more detailed modeling of the underlying complex system may prove more effective. The purpose of this article is to answer this question, and, in order to do this, most of this article’s content is devoted to the latter kind of approach, because the former kind is described in detail in the existing literature. The specific approach of the latter kind that we investigate is based on, first, developing a heterogeneous knowledge graph model of the overall complex system, and, second, developing a procedure that quantifies salience using the strength of the skill-dependency chains that link a course to a specified job. To evaluate our methods, we perform a human subjects study in which we leverage the domain expertise of fifty participants. The results of the study demonstrate that the latter approach produces career-motivated course recommendations, as well as accompanying explanations, which systematically exceed those produced by the former approach, in terms of both their quality and usability. Full article

The paper sought to examine the role of collaboration in sustaining citizen science activities and projects in academic libraries. The study applied a quantitative approach and a survey design to assess knowledge and understanding of citizen science by academic librarians to advance research relevant to SDGs. A standardised questionnaire was distributed to 185 academic librarians affiliated with the Higher Education and Libraries Interest Group (HELIG). The survey yielded a response rate of 34% since only 63 academic librarians volunteered to participate in the completion of the questionnaire. Data was analysed using SPSS version 29. Findings revealed that citizen science is a new concept in academic libraries in South Africa. To advance the use of citizen science in contributing towards SDGs, academic librarians need to raise awareness, foster collaborations, and initiate advocacy efforts to promote and support citizen science activities. The research further revealed that a work-integrated learning and community engagement department should be established within the library to advocate for citizen science activities. There is a need to visit schools to introduce citizen science at the grassroots level to increase the visibility of the field and to lay a foundation for scientific literacy at an early stage. Although the research setting was in academic libraries, for future research, it will be beneficial to conduct such a study in a public library setting to achieve varying perspectives from the community members where the concept of citizen science emanates. Full article

The deployment of autonomous systems in human environments demands sophisticated mechanisms for recognizing and preventing harm. This paper proposes an innovative discovery method for identifying harm-relevant features through the systematic analysis of thick harm verbs—semantically and pragmatically rich linguistic concepts like “puncture”, “crush”, or “poison” that encode both the mechanics and normative evaluations of specific harm types. By analyzing thick harm verbs to extract the information they encode, we can systematically identify the objects, properties, mechanisms, and contextual conditions that autonomous systems need to track to recognize and prevent harm. We demonstrate how this discovery method can be implemented with the support of large language models as analytical assistance tools, showing how human analysts can operationalize the framework with current technology. The resulting feature specifications discovered through this method provide foundations for constructing harm ontologies that bridge abstract ethical principles and concrete system requirements, addressing a critical gap in autonomous systems design while maintaining explanatory transparency essential for safe deployment in human environments. Full article

This research focuses on ontology-driven conversational agents (CAs) that harness large language models (LLMs) and their mediating role in performing collective tasks and facilitating knowledge-sharing capabilities among multiple healthcare stakeholders. The research addresses how CAs can promote a therapeutic working alliance and foster trustful human–AI collaboration between emergency department (ED) stakeholders, thereby supporting collaborative tasks with healthcare professionals (HPs). The research contributes to developing a service-oriented human–AI collaborative framework (SHAICF) to promote co-creation and collaborative learning among patients, CAs, and HPs, and improve information flow procedures within the ED. The research incorporates agile heavy-weight ontology engineering methodology (OEM) rooted in the design science research method (DSRM) to construct an ontological metadata model (PEDology), which underpins the development of semantic artifacts. A customized OEM is used to address the issues mentioned earlier. The shared ontological model framework helps developers to build AI-based information systems (ISs) integrated with LLMs’ capabilities to comprehend, interpret, and respond to complex healthcare queries by leveraging the structured knowledge embedded within ontologies such as PEDology. As a result, LLMs facilitate on-demand health-related services regarding patients and HPs and assist in improving information provision, quality care, and patient workflows within the ED. Full article

This study conducts a systematic bibliometric review of artificial intelligence (AI)-based approaches to tacit knowledge extraction and management. Drawing on data retrieved from Scopus and Web of Science, this study analyzes 126 publications published between 1985 and 2025 using VOSviewer and Biblioshiny to map citation networks, keyword co-occurrence patterns, and thematic evolution. The results identify nine major clusters spanning machine learning, natural language processing, semantic modeling, expert systems, knowledge-based decision support, and emerging hybrid techniques. Collectively, these findings indicate a field-wide shift from manual codification toward scalable, context-aware, and semantically enriched approaches that better support tacit knowing in organizational practice. Building on these insights, the paper introduces the AI–Tacit Knowledge Co-Evolution Model, which situates AI as an epistemic partner—augmenting human interpretive processes rather than merely codifying experience. The framework integrates Polanyi’s concept of tacit knowing, Nonaka’s SECI model, and sociotechnical learning theories to elucidate how human–AI interaction transforms the dynamics of knowledge creation. The review consolidates fragmented research streams and provides a conceptual foundation for guiding future methodological development in AI-enabled tacit knowledge management. Full article

Artificial Intelligence, now commonly called AI, is having an increasingly big impact on society. There are fears that may be negatives or downsides, especially when Artificial Intelligence is used unethically. But how are humans guiding these machines to know whether the choice, the decision, is ethical? Since 2007, one way to check the ethicality of any choice has been to apply the JUSTICE model. This framework helps practitioners decide whether a specific action is or is not ethical by looking through one or more of the seven JUSTICE lenses: Justice, Utilitarian, Spiritual Values, TV rule or Transparency, Influence, Core, and Emergency. Now, in this era of increasing prevalence of Artificial Intelligence, with humans making decisions often together with machines, can the JUSTICE framework still be useful? Yes, it can. We look at each of those seven components. Each may give guidance in some situations. Of the seven, it seems that T or the TV test is most likely to give guidance in this new era. Full article

Small- and medium-sized enterprises (SMEs) are increasingly embracing digital transformation (DT) to remain competitive; however, the enabling role of knowledge management (KM) remains underexplored. This systematic literature review investigates how KM supports DT in SMEs, focusing on strategic processes, tools, barriers, and policy contexts. A structured search was conducted in Google Scholar, Scopus, and Web of Science using the string: (“knowledge management” OR “KM”) AND (“digital transformation” OR “DT”) AND (“small and medium enterprises” OR “SME”). The search yielded 32,547 results, from which 19 studies met the eligibility criteria (English, 2020–2025, KM–DT focus, clear methodology). Results indicate that KM supports DT primarily through change management (31.58%), innovation enablement (21.05%), as well as improved decision-making and agility (15.79%). The most cited tools include KM systems, AI/analytics, and collaborative platforms. Major barriers include limited resources, lack of digital skills, and poor KM culture. Critical success factors identified are leadership commitment (26.32%) and strategic alignment (21.05%). Theoretical foundations are dominated by the Resource-Based View and Dynamic Capabilities Theory. While KM is proven to be a strategic driver of DT in SMEs, more empirical and policy-grounded studies are needed. This review provides a framework to guide future research and inform SME practitioners and policymakers. Full article

Almost all societal grand challenges, whether concerning the environment, health, well-being, or the development of sustainable economic models, have at their heart a need to understand people’s behaviour. However, uniting data and insights across disparate fields requires an explicit and shared understanding of concepts, variables, and ideas (e.g., how to characterise and differentiate behaviours). Ontologies provide a mechanism for creating this explicit and shared understanding and are starting to be developed and used in the social and behavioural sciences. This paper proposes an online co-design approach to use and develop ontologies of behaviour to specify the characteristics of behaviour (e.g., habitual, changeable, effortless) and studies that investigate behaviour as part of a project designed to understand how behaviours are related. We report on our experience of collaborative co-development of ontologies using real-time interactive tools and reflect on the benefits and challenges of our approach. We also offer a set of recommendations for researchers interested in applying such methods to co-develop ontologies. The work contributes to efforts to understand the characteristics of behaviour and enable these to be used to understand questions about behaviour (e.g., is poor sleep associated with greater engagement in habitual behaviours?). Full article

This article presents a case study showing the development of a chatbot, named Selene, in a Software-as-a-Service platform for behavioral analysis using Retrieval-Augmented Generation (RAG) integrating domain-specific knowledge and enforcing adherence to organizational rules to improve response quality. Selene is designed to provide deep analyses and practical recommendations that help users optimize organizational behavioral development. To ensure that the RAG pipeline had updated information, we implemented an Extract, Transform, and Load process that updated the knowledge base of the pipeline daily and applied prompt engineering to ensure compliance with organizational rules and directives, using GPT-4 as the underlying language model of the chatbot, which was the state-of-the-art model at the time of deployment. We followed the Generative AI Project Life Cycle Frameworkas the basic methodology to develop this system. To evaluate Selene, we used the DeepEval library, showing that it provides appropriate responses and aligning with organizational rules. Our results show that the system achieves high answer relevancy in 78% of the test cases achieved and a complete absence of bias and toxicity issues. This work provides practical insights for organizations deploying similar knowledge-based chatbot systems. Full article

In recent advancements within natural language processing (NLP), lexical networks play a crucial role in representing semantic relationships between words, enhancing applications from word sense disambiguation to educational tools. Traditional methods for constructing lexical networks, however, are resource-intensive, relying heavily on expert lexicographers. Leveraging GPT-4o, a large language model (LLM), our study presents an automated, scalable approach to creating multi-relational Japanese lexical networks for the general Japanese language. This study builds on previous methods of integrating synonyms but extends to other relations such as hyponymy, hypernymy, meronymy, and holonomy. Using a combination of structured prompts and graph-based data storage, the model extracts detailed lexical relationships, which are then systematically validated and encoded. Results reveal a substantial expansion in network size, with over 155,000 nodes and 700,000 edges, enriching Japanese lexical associations with nuanced hierarchical and associative layers. Comparisons with WordNet show substantial alignment in relation types, particularly with soft matching, underscoring the model’s efficacy in reflecting the multifaceted nature of lexical semantics. This work contributes a versatile framework for constructing expansive lexical resources that hold promises for enhancing NLP tasks and educational applications across various languages and domains. Full article

Mobile learning (m-learning) technologies are gaining popularity in universities but not uniformly across institutions because of cognitive, affective, and behavior obstacles. This research tested and applied an expansion of the Technology Acceptance Model (TAM) with technostress (TECH) and resistance to change (RTC) as affective obstacles, as well as the core predictors of perceived usefulness (PU), perceived ease of use (PE), and perceived risk (PR). By employing a cross-sectional survey of Greek university students (N = 608) and partial least squares structural equation modeling (PLS-SEM), we tested direct and indirect impacts on behavioral intention (BI) to apply m-learning applications. The results affirm that PU and PE are direct predictors of BI, while PR has no direct impact on BI but acts indirectly through TECH and RTC. Mediation is partial in terms of PE and PU and indirect-only (complete) in terms of PR with respect to the impact of affective states on adoption. Multi-group comparisons found differences in terms of gender, age, confidence, and years of use but not frequency of use, implying that psychological and experiential characteristics have a greater impact on intention than habitual patterns. These results offer theory-driven and segment-specific guidelines for psychologically aware, user-focused m-learning adoption in higher education. Full article

The revealed capability of generative AI tools can significantly transform the way knowledge work is conducted. With more tools being built and implemented, generative AI-aided knowledge work starts to emerge as a new normal, where knowledge workers shift a significant portion of their workloads to the tools. This new normal can lead to many concerns and issues including workers’ mental health, employees’ confusion in production, and potential spreading misknowledge. Considering the substantial portion of knowledge work in the US economy, this paper calls for more research to be conducted on this important area. This paper synthesizes relevant economic and behavioral research findings in the AI automation field and opinions of field experts, and presents a comprehensive framework, “generative AI-aided knowledge work”. This framework theoretically addresses concerns such as job replacement and organizational and behavioral factors in using generative AI and provides directions for future research and guidelines for practitioners in incorporating generative AI tools. This is one of the early attempts to provide a comprehensive overview of generative AI’s impacts on knowledge workers and production. It has the potential to seed future research in many areas such as countering misknowledge and employees’ mental health. Full article

As artificial intelligence (AI) technologies are increasingly integrated into emergency management, ethical considerations demand greater attention. Essential components of comprehensive emergency management include mitigation, preparedness, response, and recovery, which should serve as the foundation for integrating AI-driven science and technologies to effectively safeguard populations and infrastructure in times of crisis. This paper reviewed the ethical challenges of AI in emergency management in terms of critical issues, best practices, applications, emerging ethical considerations, and strategies addressing ethical challenges. Three core ethical themes are identified: algorithmic bias; privacy, transparency and accountability; and human–AI collaboration. This paper thoroughly analyzed the associated ethical challenges, reviewed the theoretical frameworks and proposed strategies to mitigate ethical challenges by strengthening the audits of algorithms, enhancing transparency in AI decision-making, and incorporating stakeholder engagement. Finally, the importance of creating policies to govern AI ethics was discussed. Full article

Generative artificial intelligence (AI), in particular large language models such as ChatGPT, have reached public consciousness with a wide-ranging discussion of their capabilities and suitability for use in various professions. Following the printing press and the internet, generative AI language models are the third transformative technological invention, with truly cross-sectoral impact on knowledge transmission and knowledge generation. While the printing press allowed for the transmission of knowledge that is independent of the physical presence of the knowledge holder, with publishers emerging as gatekeepers, the internet added levels of democratization, allowing anyone to publish, along with global immediacy. The development of social media resulted in an increased fragmentation and tribalization in online communities regarding their ways of knowing, resulting in the propagation of alternative truths that resonate in echo chambers. It is against this background that generative AI language models have entered public consciousness. Using the strategic foresight methodology, this paper will examine the proposition that the age of generative AI will emerge as an age of public ignorance. Full article

The human brain is one of the most complex parts of the human body. Its function has been studied extensively in biology and medicine. Along this line, applied mathematics plays a crucial role through the formulation and analysis of mathematical models. A student’s ability to learn is an important aspect of these studies. In this paper, a theoretical mathematical model is presented to study the brain’s ability to learn, with parameters such as human intelligence, the expected amount of knowledge a student seeks to acquire, and the tendency to forget. A parametric study of the obtained model is conducted, and by taking into account actual data from the literature, the values of the parameters that fit these data are derived, demonstrating the validity of the model. The findings of this study indicate that the proposed model accurately embodies the core principles of mastery learning and offers a practical framework that educators can employ to improve instructional planning, thereby optimizing students’ readiness for examinations scheduled on fixed dates. Full article