Journal of Information & Knowledge Management

Understanding trends is helpful to identify future behaviours in the field, and the roles of people, places, and institutions in setting those trends. Although traditional clustering strategies can group articles into topics, these techniques do not focus on topics over limited timescales; additionally, even when articles are grouped, the generated results are extensive and difficult to navigate. To address these concerns, we create an interactive dashboard that helps an expert in the field to better understand and quantify trends in their area of research. Trend detection is performed using the time-biased document clustering method. The developed and freely available web application enables users to detect well-defined trending topics by experimenting with various levels of temporal bias — from detecting short-timescale trends to allowing those trends to spread over longer times. Experts can readily drill down into the identified topics to understand their meaning through keywords, example articles, and time range. Overall, the interactive dashboard will allow experts in the field to sift through the vast literature to identify the concepts, people, places, and institutions most critical to the field.

Neolearning was developed as a teaching and learning methodological platform for the Networked University (NU) model in order to promote experiential and expansive learning for competence development and facilitate the transfer of learning into work practice. The objective of this paper is to analyse the Neolearning as a methodological support for the NU model. To achieve this objective, a qualitative approach was chosen for exploratory and descriptive purposes, through a narrative literature review and multiple case study. This paper presents the main approaches and theories that underlie the NU and the Neolearning methodology. It also presents the guidelines of the NU, the Neolearning cycle, the mechanisms that facilitate the application of Neolearning in the NU, and the analysis of these mechanisms in three case studies. In conclusion, it is confirmed that the Neolearning methodology is aligned with the NU model and attends to the objective of support the teaching and learning processes. Neolearning has been validated in empirical research, both in academic and corporate universities (CUs), as well as in undergraduate courses, graduate programs, and online courses, in face-to-face or remote learning modalities.

Business competition nowadays involves entire supply chains (SCs) rather than individual firms, and companies, with appropriate ways, need to coordinate and co-manage knowledge resources, activities, and innovative efforts in collaboration with their partners for improved competitiveness. Knowledge management (KM) practices and their adoption in single firms to manage inter-firm relationships can be of great help, but the current studies on this aspect are very fragmented. There is still insufficient knowledge of what practices are adopted to manage knowledge and how intensely these are used in inter-firm relationships and especially in supply chains. This study contributes to filling this gap by measuring the level of adoption and intensity of use of KM practices in companies. Also, it provides a comparison between their use at the intra- and inter-firm level. A survey of a sample of European large- and medium-sized manufacturing firms was used. A descriptive statistical analysis and a correlation analysis were performed. The study confirms that KM practices are used in companies, but with a varied level of adoption and intensity of use among firms. Especially, it resulted that KM is less used at an inter-firm level, and there is a relatively higher variation among firms in the inter-firm use rather than the internal context. On the other hand, the correlation analysis revealed that the more intensely firms used KM internally, the more likely they will intensely employ them to manage knowledge in SC relationships. The usefulness of the study lies in the fact that improving the awareness of business managers and consultants, both at firm and inter-firm, can facilitate introduction and development of KM programs.

In Ethiopia, healthcare service delivery faces various challenges, specifically in relation to prescribing the right medicine to the right patient at the right time. Thus, patients are exposed to challenges ranging from staying on treatment plans longer than necessary, leaving treatment too early and dying of complications. This paper aims to explore the trends, challenges and opportunities of applying big data analytics (BDA) in precision medicine in other locations around the globe and taking lessons for Ethiopia through a systematic review of 14 peer-reviewed articles from three popular databases. The findings indicate that cancer, epilepsy and systemic diseases altogether are areas currently getting big attention. The challenges are attributed to the nature of health data, failure in collaboration of data sharing, ethical and legal issues, interoperability of systems, poor knowledge skills and culture, and poor infrastructure. In addition to the adoption of modern technologies (new and experimental methods, cloud computing, Internet of Things, social networks, etc.), Ethiopia’s government initiative to promote private technological firms could be an opportunity to use BDA in precision medicine in Ethiopia.

Global warming refers to the long-term heating of the earth, including melting of the glaciers and the expected rise in seawater level that could threaten a large number of areas and communities around the world. Social media has a big influence on people’s perception and understanding of environmental issues and challenges. In this study, we examine the impact of misinformation and social noise on people’s perception of global warming during critical environmental events. The study used data analytic techniques such as sentiment analysis and topic modelling to assess the level of objectivity and subjectivity in the discussion about global warming and examine the presence of certain topics that fit the definition of social noise and misinformation. Data collection was limited to the hashtag “global warming” captured during the months there was a spike in global warming tweets during the period from 2012-2022. The results from the sentiment analysis have shown a 62.46% subjectivity score compared to 37.54% objectivity score, indicating that most of the tweets posted were based on users’ personal opinion and perception of global warming. Topic modelling showed a prevalence of social noise in the tweets indicating the presence of social noise and thus aids in understanding the factors that lead to misinformation creation. The presence of social noise questions the credibility of the tweets and its contents.

Knowledge representation through metadata is crucial to successful knowledge management and ensuring access. Libraries, especially those associated with universities and colleges, have been long engaged in important activities of developing, adapting, implementing, and managing descriptive and administrative metadata, bibliographic authority control, etc. A substantial amount of research exists in knowledge representation practices of libraries that operate in North America and Western Europe. However, some other regions of the world, in particular the Arabian Gulf region which includes Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates, are currently underrepresented in this research. The exploratory project presented in this paper aimed to address this gap with the goal of developing an understanding of the current state of knowledge representation (including descriptive metadata practices, identity management, use of knowledge organisation systems, and more) in academic libraries of Kuwait, Oman, and Qatar, as well as plans and preparedness of these institutions and their staff for future developments in this area to facilitate discovery of resources, specifically in the aggregated digital environment. The qualitative study used semi-structured interviews of metadata managers at a representative sample of regional university libraries. The findings provide insights into this previously under-researched area and contribute to understanding of related knowledge management barriers and perspectives on a global scale. Implications for practice and research are discussed.

To provide solutions to the world’s global challenges, there is an urgent demand for wise organisations, wise leadership, wise workers, and most importantly, for wise actions. Since the mid-1990s, Knowledge Management (KM) as a discipline and practice has emerged internationally, and it has passed through several phases of development. Simultaneously, in the last four decades we have experienced a growth of wisdom research and intensified discourses about Wisdom Management (WM) as a possible venue for dealing with wicked problems. The dilemma is whether the current sixth phase of KM is able to address the global problems of the world. Therefore, this conceptual paper seeks to answer the question if WM will complement or replace KM. This paper explores the evolutionary phases of KM; it presents the discourses about WM, and it discovers the presence of wisdom in the KM literature (1995–2022). The research approach is explorative and inductive. Methods of Computational Social Sciences and RStudio as an Integrated Development Environment (IDE) are utilised for analysing, visualising, and interpreting the data. This paper analyses 255 wisdom-related keywords that emerged from 39 sources written by 50 authors. The three data analyses methods are: word cloud data analysis; dictionary-based data analysis, and bipartite network analysis. The findings are presented in word clouds; keywords frequency; bipartite network, and in a synthesised framework to show the similar and different concepts of KM and WM. The novelty value of this paper is the result of the bipartite network analysis that allows to identify how authors and wisdom-related keywords are connected with the same word nodes. This paper contributes to KM because it is the first study to explore and illustrate the presence of wisdom attributes in the KM literature.

Managing flood-related data to assist in the disaster management is a critical process of high importance during a flood disaster. These data are heterogeneous and can be provided from different data sources, and integrating them is a challenging task which allows inferring new information that helps in limiting the consequences of a flood. In this paper, we propose a novel approach that manages heterogeneous flood-related data based on semantic web techniques and helps in limiting the damage caused by floods. We first propose an ontology that is used to formally describe the flood-related data, and we construct our knowledge graph through integrating heterogeneous data using the proposed ontology. Then, we propose a reasoning approach using SHACL rules to infer new information that helps manage the flood disaster or anticipate future events. The experimental evaluations of our proposed approach are conducted on a real case study in flood disaster management with the aim of generating evacuation priorities. The results show that the proposed approach succeeds in managing heterogeneous flood-related data and in generating evacuation priorities in a very short time.

The increasing recognition of the negative impact of knowledge risks on the operations of small and medium-sized enterprises (SMEs) has led to a need for effective methods to evaluate and manage these risks. SMEs often adopt a reactive approach to risk management, which may not be sufficient to address the complex and evolving nature of knowledge risks. This study aims to use the grey-DEMATEL technique to evaluate operational knowledge risks in SMEs, with a focus on identifying the most critical risks and their potential causes and effects. The results revealed 11 operational knowledge risks, and found that outsourcing risks are the most critical among them. Communication risks and improper knowledge application were also found to be significant. Additionally, these risks were successfully categorised into effect risks (including relational risk, espionage, knowledge waste, and continuity risk) and causal risks (including knowledge waste, risks related to knowledge gaps, and the risk of using obsolete or unreliable knowledge). These categories provide a framework for understanding the potential causes and effects of operational knowledge risks and may be useful for designing risk management strategies. To the best of the authors’ knowledge, this is one of the first studies to use the grey-DEMATEL technique to evaluate operational knowledge risks in SMEs.

The continuous propagation of Artificial Intelligence (AI) techniques has led to engineering of so-called “humanoid” socio-technical settings, which include mimicking cognitive and social skills. With the advent of cyber-physical systems Digital Twins have become baseline representations of systems integrating cognitive and social behaviour models. When AI components become part of those representations capturing social relations and reflecting on behaviour, they are termed Digital Selves, and require specific development techniques. In this paper, we introduce a Knowledge Management approach to investigate the emergence of socially reflective behaviour like awareness and consciousness in artificial agents. We identify and explain mandatory properties of Digital Selves relevant for modelling digital societies that need to be studied and become part of a transparent and explainable knowledge base for trans-human developments.

Quantum physics, with its divergence from conventional human conceptual frameworks, presents an intriguing challenge in comprehension and application. This enigmatic field appears incongruent with everyday experiences and resists interpretation through even the most rigorous mathematical formulations. Yet, the impending advent of commercial quantum computing, anticipated within the next decade, offers a unique perspective for understanding these complexities. Drawing from historical instances where the emergence of novel technologies catalysed the evolution of elucidating metaphors, this paper postulates the synchronous rise of quantum computing with the development of metaphors to explicate quantum effects. These metaphors hold significant potential to illuminate poorly understood areas of organisational behaviour, offering new lenses to view, interpret and analyse such phenomena. Three case studies, namely the agile transformation of ING Bank, the seminal Hawthorne experiments and two large-scale training programs implemented by Starbucks, are deployed to substantiate the proposed use of quantum metaphors as supplementary interpretative tools. The emergence of this new metaphorical language, intrinsically tied to the unfolding of quantum computing technology, may herald the advent of novel research methodologies and paradigms. This exploratory study serves as a precursor to further research in organisational studies through the lens of quantum metaphors, opening doors to a more profound understanding of complex organisational phenomena.