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Exogenous shocks, such as COVID-19, significantly change fundamental premises on which economies and individual organizations operate. The light-asset nature of digital technologies provides the potential to not only facilitate an immediate crisis response, but also to catalyze novel innovation types to address the societal and economic changes caused by exogenous shocks. As digital innovation became a relevant part of organizations' COVID-19 responses, and given that a corresponding structured knowledge base did not exist, we found the need to better understand crisis-driven digital innovation. Drawing on prior knowledge from crisis management and organizational ambidexterity as a theoretical lens, we present four patterns of crisis-driven digital innovation, classified along two dimensions: (1) driven by a sense of urgency or ambition and (2) focusing on exploitative or explorative innovation. Based on a thorough analysis of digital innovation cases during the COVID-19 crisis, we illustrate and discuss these four patterns and their emerging properties to explain how and why they led to digital innovation in the context of the crisis. Our work contributes to the explanatory knowledge on digital innovation in times of crisis, helping researchers and practitioners to understand and develop digital innovation in response to exogenous shocks.
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Complexity, heterogeneity, schemaless-ness, data visualization, and extraction of consistent knowledge from Big Data are the biggest challenges in NoSQL databases. This paper presents a general semantic NoSQL Application Program Interface that integrates and converts NoSQL databases to semantic representation. The generated knowledge base is suitable for visualization and knowledge extraction from different Big Data sources. The authors use a case study of the COVID-19 pandemic prediction and other weather occurrences in various parts of the world to illustrate the suggested API. The Authors find a correlation between COVID-19 spread and deteriorating weather. According to the experimental findings, the API's performance is enough for heterogeneous Big Data.
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Non-destructive testing (NDT) is a quality control measure designed to ensure the safety of products according to established variability thresholds. With the development of advanced technologies and a lack of formalised knowledge of the state-of-the-art, the National Composites Centre, Bristol, has identified that the increasing complexity of composite products will lead to some severe inspection challenges. To address the apparent knowledge gap and understand system complexity, a formulaic approach to introduce intelligence and improve the robustness of NDT operations is presented. The systemic development of a high-fidelity knowledge base (KB) involves the establishment of a capability matrix that maps material, component, and defect configuration to the capabilities and limitations of selected detection methods. Population and validation are demonstrated through the experimental testing of reference standards and evaluated against an assessment criteria. System complexity in ultrasonic testing operations focusses on capturing the inherent risks in inspection and the designation of evidence-based path plans for automation platforms. Anticipated deployment of the validated applicability data within the KB will allow for road-mapping of the inspection technique development and will provide opportunities for knowledge-based decision making. Moreover, the KB highlights the need for Design for Inspection, providing measurable data that the methodology should not be ignored.
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The COVID-19 pandemic is completely changing the transport customs of city residents. It has decreased the number of travels and has affected changes in the division of transport means. This article presents a case study of the city of Warsaw, attempting to describe the process of changes in the use of public transport in daily trips in the following months of the pandemic. Statistical data on the public transport offer, number of passengers, and tickets sold in 2017–2021, which are available in monthly and annual bulletins issued by the public transport organizer, were used. The knowledge base was supplemented with the results of surveys conducted among the city’s residents. The obtained data were organized and analyzed using descriptive statistics methods. The study findings reveal that the lower use of public transport for travel during the COVID-19 pandemic is mainly due to the imposed limits on the number of passengers and is also linked to changes in the structure of the tickets purchased, especially a significant decrease in the sale of long-distance tickets, which implies the loss of a significant number of most valuable, regular users. It was also observed that the appraisal of public transport did not deteriorate, which allowed to expect with optimism the return of passengers after the pandemic. Therefore, a package of possible steps to be taken to restore confidence in public transport and to enable return of lost passengers is presented. The results of the analyses show how easily passengers can be lost and why it is so important to ensure the functioning of public transport even in crisis situations such as a pandemic. These results can also be applied in transport policy updates.
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This paper presents a bibliometric analysis of COVID-19-related research in business economics. The current status of research on economic management in COVID-19 is shown through descriptive statistics. The corresponding knowledge maps are obtained based on keyword clustering analysis, and research topics of interest to Chinese and foreign readers are identified. This paper finds that the impact of COVID-19 on business economics is mainly manifested in six major themes, namely COVID-19 and crisis management, COVID-19 and supply chain, COVID-19 and digitalization, COVID-19 and economic development, COVID-19 and organizational management, and COVID-19 and sustainable development. Based on these research foundations, this paper proposes a research framework for economic management under the influence of COVID-19. It describes the current research status, research directions, and future topics of six key research themes from macro, meso, and micro perspectives, to provide a knowledge base for research and practice in the field of economic management in the post-pandemic era.
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The education sector, among others, was severely affected by the coronavirus disease 2019 (COVID-19) pandemic. Because mathematics has always been singled out as a subject that needs more verbal communication and interaction, rapid adjustments had to be made by mathematics lecturers in higher education institutions to try and facilitate normal teaching and learning remotely through emergency open distance methods. Lecturers were forced to examine prevailing practices with a view to creating innovative and workable solutions to the emergency challenges without compromising the quality previously experienced during face-to-face classroom interactions. The article developed through a simple technology a conceptual framework for emergency remote teaching (ERT) in an emergency techno-response pedagogy (ETRP). The key was to demonstrate an innovative instructional strategy for teaching mathematics using a simple technology instead of an advanced or complicated mathematics software in the move from face-to-face to fully online teaching during a crisis. A development qualitative virtual case study was conducted that involved observing live and recorded mathematics lectures and interviewing an innovative lecturer of mathematics in the delivery of complex numbers at a graduate school in South Africa. The facilitation of the lesson through a simple and inexpensive technology (Microsoft OneNote) guided the development of a conceptual framework for ERT within an ETRP. The Context, Input, Process, and Product (CIPP) evaluation model was used as a theoretical framework to guide the analysis and conceptualisation of the lessons. Results provided guidelines through a conceptual framework for ERT that included a unique model of a lesson plan and advantages of using a simple technology in ERT instead of advanced mathematical software. The article contributes to the knowledge base in planning future ERT interventions.
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In recent years, telehealthcare systems (TSs) have become more and more widespread, as they can contribute to promoting the continuity of care and managing chronic conditions efficiently. Most TSs and nutrition recommendation systems require much information to return appropriate suggestions. This work proposes an ontology-based TS, namely HeNuALs, aimed at fostering a healthy diet and an active lifestyle in older adults with chronic pathologies. The system is built on the formalization of users’ health conditions, which can be obtained by leveraging existing standards. This allows for modeling different pathologies via reusable knowledge, thus limiting the amount of information needed to retrieve nutritional indications from the system. HeNuALs is composed of (1) an ontological layer that stores patients and their data, food and its characteristics, and physical activity-related data, enabling the inference a series of suggestions based on the effects of foods and exercises on specific health conditions;(2) two applications that allow both the patient and the clinicians to access the data (with different permissions) stored in the ontological layer;and (3) a series of wearable sensors that can be used to monitor physical exercise (provided by the patient application) and to ensure patients’ safety. HeNuALs inferences have been validated considering two different use cases. The system revealed the ability to determine suggestions for healthy, adequate, or unhealthy dishes for a patient with respiratory disease and for a patient with diabetes mellitus. Future work foresees the extension of the HeNuALs knowledge base by exploiting automatic knowledge retrieval approaches and validation of the whole system with target users.
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Education is an important domain that may be improved by analyzing the sentiments of learners and educators. Evaluating the sustainability of the education system is critical for the continuous improvement and satisfaction of the learner’s community. This research work focused on the evaluation of the effectiveness of the online education system that has been adopted during the COVID-19 pandemic. For this purpose, sentiments/reviews of learners were collected from the Twitter website regarding the education domain during COVID-19. To automate the process of evaluation, a hybrid approach was applied that used a knowledgebase of opinion words along with machine learning and boosting algorithms with n-grams (unigram, bigram, trigram and combination of all these n-grams). This automated approach helped to evaluate the transition of the education system in different circumstances. An ensemble classifier was created in combination with a customized knowledgebase using classifiers that individually performed best with each of the n-grams. Due to the imbalanced nature of the data (tweets), these operations were performed by applying the synthetic minority oversampling technique (SMOTE). The obtained results show that the use of a customized knowledgebase not only improved the performance of the individual classifiers but also produced quality results with the ensemble model. As per the observed results, the online education system was not found sustainable as the majority of the learners were badly affected due to some important aspects (health issues, lack of training and resources).
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Purpose>This study aims to answer the question of how business models (BMs) maintain stability while coping with environmental uncertainties. This study proposes a dynamic co-evolution of knowledge management and business model transformation based on a comparative analysis of the focal firms’ BMs and their main partners in two e-commerce ecosystems in China.Design/methodology/approach>The open data of listed companies regarding the introduction of emerging topics on the transformation tendency of BMs in the post-COVID-19 business world is qualitatively analysed. The theoretical foundation is based on a critical review of the literature.Findings>Three aspects of the co-evolution between knowledge management and business model transformation are introduced. These three aspects are as follows: knowledge integration helps with multi-system business integration and decision-making collaborations;knowledge sharing helps to enhance cognitive ability and network value based on businesses;and the creation of new knowledge helps enrich the knowledge base and promote the transformation of BMs.Research limitations/implications>Solely attributing a firm’s ability to cope with environmental uncertainties to its business model weakens the importance of its knowledge management. This study argues that the co-evolution between knowledge management and business model transformation also plays a key role in a firm’s response to issues post-COVID-19.Originality/value>This study calls for the development of a normative theory of co-evolution between knowledge management and business model transformation, implying uncharted territories of knowledge management based on interaction with business model designs in e-business ecosystems.
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[...]mitigating the threat posed by AMR requires a recognition of how embedded social structures and incentives drive antimicrobial use across sectors. [...]escalating commitments through national AMR action plans, which outline each country's AMR goals and planned actions, will likely increase the effectiveness of global AMR efforts. Fifth, like the Intergovernmental Panel on Climate Change guiding the Paris Agreement, ongoing AMR action would be best informed by a regular and independent stock-taking to evaluate existing measures and advise on evidence-informed adjustments.11,12 This endeavor must (1) recognize that different ways of knowing constitute the global knowledge base, (2) ensure that using evidence to inform adjustments that work does not detract from the inherently political questions of works for what purpose and for whose benefit, and (3) come with a commitment to equitable evidence generation and prioritization. Striking a panel to assess the global knowledge base on these terms will ensure that global, regional, and national goals and policies are continually informed by the best available evidence and are in line with leading practices.12 Finally, an enduring international legal agreement could institutionalize requires new legal mechanisms beyond those available through the World Health Organization, the Food and Agriculture Organization of the United Nations, the World Organization for Animal Health, and the United Nations Environment Program, which are limited to the area-specific mandates of each institution.
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PurposeThe paper aims to present the development of conceptualization of coronavirus disease 2019 (COVID-19) based on associations with other articles on English edition of Wikipedia. The main goal of the paper is to study the social organization of knowledge about COVID-19 within the Wikipedia community of practice.Design/methodology/approachThe methodological approach taken in this study was based on the application of Moscovici's theory of social representations to Wikipedia's knowledge organization system (KOS). Internal links in the Wikipedia article about COVID-19 were considered anchors in its social representations. Each link in the introductory part of the article was considered an indicator of the semantic relationship between COVID-19 and other concepts from Wikipedia's knowledge base. The subject of this study was links extracted from all revisions of the COVID-19 article between February and September 2020. Qualitative and quantitative analyses were performed on these conceptual structures using both synchronic and diachronic approaches.FindingsIt was found that the evolution of anchors in the Wikipedia article on COVID-19 was in line with the mechanism of symbolic coping related to infectious disease. It went through stages of divergence, convergence and normalization. It shows that this mechanism governs the social organization of knowledge related to COVID-19 on Wikipedia.Originality/valueNo studies have been devoted to the image of COVID-19 as presented by the evolution of links in Wikipedia and its implications for knowledge organization (KO).
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Information related to the COVID-19 pandemic ranges from biological to bibliographic, from geographical to genetic and beyond. The structure of the raw data is highly complex, so converting it to meaningful insight requires data curation, integration, extraction and visualization, the global crowdsourcing of which provides both additional challenges and opportunities. Wikidata is an interdisciplinary, multilingual, open collaborative knowledge base of more than 90 million entities connected by well over a billion relationships. It acts as a web-scale platform for broader computer-supported cooperative work and linked open data, since it can be written to and queried in multiple ways in near real time by specialists, automated tools and the public. The main query language, SPARQL, is a semantic language used to retrieve and process information from databases saved in Resource Description Framework (RDF) format. Here, we introduce four aspects of Wikidata that enable it to serve as a knowledge base for general information on the COVID-19 pandemic: its flexible data model, its multilingual features, its alignment to multiple external databases, and its multidisciplinary organization. The rich knowledge graph created for COVID-19 in Wikidata can be visualized, explored, and analyzed for purposes like decision support as well as educational and scholarly research.
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News articles generated by online media are a major source of information. In this work, we present News Monitor, a framework that automatically collects news articles from a wide variety of online news portals and performs various analysis tasks. The framework initially identifies fresh news (first stories) and clusters articles about the same incidents. For every story, at first, it extracts all of the corresponding triples and, then, it creates a knowledge base (KB) using open information extraction techniques. This knowledge base is then used to create a summary for the user. News Monitor allows for the users to use it as a search engine, ask their questions in their natural language and receive answers that have been created by the state-of-the-art framework BERT. In addition, News Monitor crawls the Twitter stream using a dynamic set of “trending” keywords in order to retrieve all messages relevant to the news. The framework is distributed, online and performs analysis in real-time. According to the evaluation results, the fake news detection techniques utilized by News Monitor allow for a F-measure of 82% in the rumor identification task and an accuracy of 92% in the stance detection tasks. The major contribution of this work can be summarized as a novel real-time and scalable architecture that combines various effective techniques under a news analysis framework.
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PurposeIn the process of Renminbi (RMB) internationalization, the heterogeneity and complexity in knowledge under the multicultural contexts have been considered as important factors that can have profound impacts on the cross-border flow of the RMB currency. Moreover, COVID-19, an exogenous shock, also triggers more in-depth reflection on the relationship between cross-border knowledge management and the financial risk governance. In addition, the needs to effectively respond to global risks and crises prompt the necessity in systematically establishing an effective cross-border knowledge management mechanism and innovatively solidifying the knowledge bases needed for the further internationalization of the RMB.Design/methodology/approachBased on the analysis on the current status of the RMB internationalization, this paper qualitatively explores some major challenges and difficulties encountered in the process of RMB internationalization from the perspectives of knowledge management and cross-cultural theories. To effectively mitigate these challenges and difficulties, discussions and recommendations centered on three main aspects: cross-cultural management;cognition;and innovation for the further development of the RMB internationalization are also presented in this paper.FindingsBased on the analysis on the cross-border knowledge management and cross-cultural perspectives, this paper identifies three major challenges and difficulties that the RMB internationalization is encountering, including: cultural heterogeneity and its adverse impacts on the communication amongst economic entities;the existence of knowledge iceberg;and the difficulty it presents to cognition and financial innovation. Meanwhile, the authors also present recommendations on the development of the cross-border knowledge management mechanism for furthering the progress of internationalizing the RMB currency.Research limitations/implicationsFrom the perspective of cross-border knowledge management, this study not only elaborates on the recommendations aimed at further promoting the RMB internationalization but also provides reference and guidance for the state, central banks and commercial banks to play better roles in furthering the RMB internationalization.Originality/valueThis paper creatively integrates the micro knowledge management into the macro process of RMB internationalization, thoroughly discusses two main challenges and difficulties encountered in the process of RMB internationalization from the unique perspective of cross-border knowledge management under the multicultural contexts and provides relevant recommendations for RMB’s further internationalization. This study also enriches the exploration of knowledge management outcome variables and further expands the research field of knowledge management.