General Knowledge Research Articles

Circular Economy: A Technological Innovation Strategy for Sustainability in Air Transport

This work aims to identify innovations, technological advances, and best practices to migrate towards a circular economy in air transport. The relevance of the research lies in the contribution to the generation of circular economy knowledge in the field of administration with application in the aeronautical industry. By analyzing literature and best practices, the circular economy could be a viable solution to reduce the use of natural resources in aircraft manufacturing and airport operations. Among the main findings are innovations and technological developments in new materials, fuels, building methods, and best practices implemented at airports to obtain energy and generate less waste. It has also been identified that applying circular economy principles in this mode of transport would contribute to sustainability efforts and increase the competitiveness of organizations in the sector.

Leveraging liminality: how the interim manager’s liminal position facilitates knowledge transfer to client organizations

Purpose With the rise of the gig economy, management positions are increasingly staffed with flexible labor, so-called interim managers. They plunge into organizations for a limited period, operating in a liminal position as partly insider, partly outsider. Although several contributions to their client organizations are acknowledged, it is unknown how the interim manager’s knowledge from previous assignments is made useful in the new context under these particular working conditions. Therefore, the purpose of this paper is to increase the understanding of how the interim manager’s knowledge is transferred to the client organization while operating from a liminal position. Design/methodology/approach This paper presents an interview-based multiple case study of six interim assignments where knowledge transfer is considered a social and context-dependent process. Findings The findings unveil the multifaceted nature of the liminal position, which consists of task orientation, time limitation, political detachment and cultural distance. These facets contribute to knowledge transfer in terms of new shared understandings and joint interests, which in turn might create new practices that augment continuous knowledge-sharing patterns. Originality/value The results contribute to the research on flexible work arrangements by shedding light on how the liminal position, predominantly depicted as an obstacle for the individual, might facilitate knowledge transfer. Through the process of knowledge generation, it is shown how a short-term engagement might enable the organization to increase its knowledge over time.

Dynamic heterogeneous federated learning with multi-level prototypes

Federated learning shows promise as a privacy-preserving collaborative learning technique. Existing research mainly focuses on skewing the class distribution across clients. However, most approaches suffer from catastrophic forgetting and classifier shift, mainly when the global distribution of all classes is extremely unbalanced and the data distribution of the client dynamically evolves over time. In this paper, we study the Dynamic Heterogeneous Federated Learning, which addresses the practical scenario where heterogeneous data distributions exist among different clients and dynamic tasks within the client. Accordingly, we propose a novel federated learning framework named Federated Multi-Level Prototypes and design federated multi-level regularizations. To mitigate classifier shift, we construct semantic prototypes to provide fruitful generalization knowledge. To maintain the model stability and consistency convergence, three regularizations are introduced as training losses, i.e., prototype-based regularization, semantic prototype-based regularization, and federated inter-task regularization. Extensive experiments show that the proposed method achieves advanced performance in various settings.

Computational Analogies of Machine Learning concept in the Oil and Gas Industry and its Transformation in the Industry 4.0 Era

The fourth Industrial Revolution, commonly known as Industry 4.0 is enabling Oil and Gas industry to transform its business process and operations and adopt digital technologies to drive innovation, enhance production and operate more efficiently. The oil and Gas industry is enhancing Knowledge Management with advancement in digital technologies such as big data analytics, and artificial intelligence to transform vast information gathered historically stored in documents and systems as well as subject matter expert experience into digital knowledge to establish advisory systems to support decisions in the Oil and Gas operations. In organizations, knowledge has become an important success element and so is the case with the oil and gas industry, which is one of the most important sectors in energy. It must be dealt with and leveraged efficiently and effectively for competing in the world market by the creation of a competitive and sustainable environment in organizations. Oil exploration is a knowledge-intensive process in which effective operations and commercial success may be accomplished by identifying and evaluating possibilities early on and making knowledge-based choices. To be productive and competitive, the oil and gas industry must embrace knowledge management processes, where professionals may play a key role in managing information to easily address issues. However, the capacity to handle knowledge cannot be instilled in a single day; it is a culture that has been created over time via specialists and their expertise. The oil and gas sector has undergone significant changes throughout the years, affecting all of its sections, including exploration, production, and refining, and has got significant implications for marketing plans, production strategies, and R&D strategies. Only efficient knowledge management strategies with the aid of big data can encompass knowledge generation, information exchange, transformation, and dissemination of information in present times. The emergence of Generative Artificial Intelligence Chatbot such as ChatGPT is transforming the way knowledge has been managed traditionally. Applying the right strategies to enhance knowledge management is vital for the Oil industry to extract, develop, maintain, preserve and disseminate knowledge for effective operations. This paper introduces and discusses the developments in knowledge management, and explores the interrelationship between AI/ML, big data, and knowledge management in the oil and gas industry.

Impact of the Use of Emerging Technologies on Organisational Knowledge-creation Capability by Task Complexity

In the context of knowledge-based economy, an organisation’s ability to create knowledge is the most important factor in maintaining its competitiveness. Collective intelligence is posited as a paramount methodology not only for the generation of knowledge pertaining to multifaceted issues but also as a fundamental pillar within the framework of organizational knowledge management. The use of emerging technologies is an important strategy for improving the organisation’s ability to create knowledge through collective intelligence by adding depth and breadth of knowledge. However, excessive use of technology often has a negative impact on organisational knowledge management. Therefore, this study aims to identify the two-sided effect of using emerging technologies (big data analytics (BDA) and online platforms) on organisational knowledge creation according to the complexity of the task. The results of our study suggest that the use of BDA technology for organisational knowledge creation should be maintained at an appropriate level in general, but it is recommended to increase the use of BDA technology for low-complexity tasks. In addition, using online platform technology is difficult to consider as a strategic way to solve high-complexity tasks, but increasing the use of BDA technology can contribute to improving the organisation’s ability to create knowledge.

إدارة المعرفة وأثرها على الرشاقة التنظيمية: دراسة تطبيقية على أمانة الرياض

The study aimed to investigate the impact of knowledge management on organizational agility at the Riyadh Municipality. The study employed a descriptive-analytical methodology, with the researcher developing a questionnaire consisting of 47 items. A total of 103 questionnaires were distributed, and 84 were retrieved, with 76 valid questionnaires for analysis, representing 73.8% of the study population. Data related to the study variables were collected and analyzed using the Statistical Package for the Social Sciences (SPSS). The results indicated that both knowledge management and organizational agility at the Riyadh Municipality were assessed at a moderate level. Furthermore, the results demonstrated a statistically significant impact of knowledge management, including its dimensions (knowledge generation, storage, sharing, and application), on organizational agility at the Riyadh Municipality. The study recommended the necessity of organizing training programs and workshops to familiarize employees at the Riyadh Municipality with various knowledge management processes and their importance in facilitating work procedures and increasing productivity.

Metodología deductiva del cuidado: descripción y prueba de los modos de investigación acerca del cuidado

ObjectiveDefine the modes of procedure of the Deductive Care Methodology (DCM) in the generation of knowledge about person's health care. MethodologyDesign and test of the DCM modes based on three phases: mapping of the MDC, generation of models from this methodology and testing of the models through studies in a clinical context. ResultsThe MDC presents five levels of abstraction with three modes broken down to 16 types. The modes are: Philosophical Mode to conceptualize and obtain generalities about reality, Mathematical Mode to operate with generalities, and Physical Mode to operationally verify, validating the results and the predictive capacity of the model. This MDC allows the creation of three models: Knowledge Model about Person Care, an ontology of care, Vulnerability Model about the person and Taxonomic Triangulation Model for knowledge management. All models generate products for computational knowledge management. In addition, the models are applied in teaching and generate research with more than a hundred participations in conferences and journals, of which five impact publications (from 2008 to 2022) classified in the categories of Nursing and Informatics are analysed. ConclusionsThe MDC collects prior knowledge to work with certainties, evidence and applying inferences that do not depend on the number of cases or inductive designs. This research presents a formal structure of the MDC with an interdisciplinary orientation between Health Sciences and Computer Sciences.

Smart tourism destinations: an overview of current research trends and a future research agenda

研究目的本研究旨在提供关于智慧目的地知识演变的概览, 并确定智慧目的地研究领域中涉及的关键问题、主要主题和子主题, 以及智慧目的地研究的研究空白, 同时提出未来研究议程以填补这些空白。研究方法本研究利用 Bibliometrix R 软件对发表的409篇智慧目的地文献进行了文献计量分析, 以全面回顾关于智慧目的地的研究, 并确定主要研究主题、文献中的空白以及未来的研究机会。研究发现基于研究结果, 本研究提出了智慧目的地研究的概念模型。该概念模型可作为进一步知识生成的基础。研究发现还为未来研究方向提供了启示, 强调了利用先进创新和数字化技术在智慧目的地各个维度上进行研究的机会。研究创新尽管过去的研究关注了智慧目的地的主题, 但本研究通过提出以可达性、可持续性、数字化、体验共创和创意为里程碑的新智慧目的地管理范式, 推进了科学知识的发展。

Masked autoencoders with generalizable self-distillation for skin lesion segmentation.

In the field of skin lesion image segmentation, accurate identification and partitioning of diseased regions is of vital importance for in-depth analysis of skin cancer. Self-supervised learning, i.e., MAE, has emerged as a potent force in the medical imaging domain, which autonomously learns and extracts latent features from unlabeled data, thereby yielding pre-trained models that greatly assist downstream tasks. To encourage pre-trained models to more comprehensively learn the global structural and local detail information inherent in dermoscopy images, we introduce a Teacher-Student architecture, named TEDMAE, by incorporating a self-distillation mechanism, it learns holistic image feature information to improve the generalizable global knowledge learning of the student MAE model. To make the image features learned by the model suitable for unknown test images, two optimization strategies are, Exterior Conversion Augmentation (EC) utilizes random convolutional kernels and linear interpolation to effectively transform the input image into one with the same shape but altered intensities and textures, while Dynamic Feature Generation (DF) employs a nonlinear attention mechanism for feature merging, enhancing the expressive power of the features, are proposed to enhance the generalizability of global features learned by the teacher model, thereby improving the overall generalization capability of the pre-trained models. Experimental results from the three public skin disease datasets, ISIC2019, ISIC2017, and PH indicate that our proposed TEDMAE method outperforms several similar approaches. Specifically, TEDMAE demonstrated optimal segmentation and generalization performance on the ISIC2017 and PH datasets, with Dice scores reaching 82.1% and 91.2%, respectively. The best Jaccard values were 72.6% and 84.5%, while the optimal HD95% values were 13.0% and 8.9%, respectively.

The Conceptualization and Measurement of Research Impact in Primary Health Care: Protocol for a Rapid Scoping Review.

The generation of research evidence and knowledge in primary health care (PHC) is crucial for informing the development and implementation of interventions and innovations and driving health policy, health service improvements, and potential societal changes. PHC research has broad effects on patients, practices, services, population health, community, and policy formulation. The in-depth exploration of the definition and measures of research impact within PHC is essential for broadening our understanding of research impact in the discipline and how it compares to other health services research. The objectives of the study are (1) to understand the conceptualizations and measures of research impact within the realm of PHC and (2) to identify methodological frameworks for evaluation and research impact and the benefits and challenges of using these approaches. The forthcoming review seeks to guide future research endeavors and enhance methodologies used in assessing research impact within PHC. The protocol outlines the rapid review and environmental scan approach that will be used to explore research impact in PHC and will be guided by established frameworks such as the Canadian Academy of Health Sciences Impact Framework and the Canadian Health Services and Policy Research Alliance. The rapid review follows scoping review guidelines (PRISMA-ScR; Preferred Reporting Items for Systematic Review and Meta-Analysis Extension for Scoping Reviews). The environmental scan will be done by consulting with professional organizations, academic institutions, information science, and PHC experts. The search strategy will involve multiple databases, citation and forward citation searching, and manual searches of gray literature databases, think tank websites, and relevant catalogs. We will include gray and scientific literature focusing explicitly on research impact in PHC from high-income countries using the World Bank classification. Publications published in English from 1978 will be considered. The collected papers will undergo a 2-stage independent review process based on predetermined inclusion criteria. The research team will extract data from selected studies based on the research questions and the CRISP (Consensus Reporting Items for Studies in Primary Care) protocol statement. The team will discuss the extracted data, enabling the identification and categorization of key themes regarding research impact conceptualization and measurement in PHC. The narrative synthesis will evolve iteratively based on the identified literature. The results of this study are expected at the end of 2024. The forthcoming review will explore the conceptualization and measurement of research impact in PHC. The synthesis will offer crucial insights that will guide subsequent research, emphasizing the need for a standardized approach that incorporates diverse perspectives to comprehensively gauge the true impact of PHC research. Furthermore, trends and gaps in current methodologies will set the stage for future studies aimed at enhancing our understanding and measurement of research impact in PHC. PRR1-10.2196/55860.

Maintenance and Its Knowledges: Functional Exploration, Biographical Supervision, and Behavioural Examination

This introductory article to the thematic collection entitled “Maintenance and its Knowledges” makes a significant departure from breakdown-centred studies. It foregrounds the epistemic virtues of maintenance, a practice that cultivates continuity, by examining a still underestimated and unexplored dimension: the forms of knowledge associated with maintenance activities. A twofold aim guides such an exploration. First, repair and maintenance interventions are examined as particular sites and moments of knowledge generation. Second, building on the scholarship dedicated to improvisation and learning dynamics in the Global South that has structured numerous works on maintenance, this article considers how it can be extended towards the Global North. Recalling that maintenance has been mostly investigated from the question of how, it stresses out the various tools involved in knowledge generation, the organisation of tasks at play in different settings and their politics. It then goes beyond the how question to embrace a more ontological perspective, focussing on what people do properly maintain, and what kinds of knowledge emerge at this particular moment. Such a line of inquiry opens up three directions towards which the production of knowledge is oriented in maintenance: functional exploration, biographical supervision, and behavioural examination.

Innovation in vine growing in a context of climate change: AKIS and map of knowledge in Central Spain

PurposeTo address the challenges of the agricultural sector, innovation is necessary. This study aims to focus on knowledge circulation as a basis to facilitate innovation in viticulture in the context of climate change.Design/methodology/approachWe have conducted interviews with viticulture stakeholders in Central Spain (Madrid region) on their perceptions and concerns about climate change, knowledge on practices to mitigate its effects on this crop and their relationship with each other for knowledge exchange. A map showing the knowledge nodes and their relationships with other stakeholders has been drawn based on the answers obtained.FindingsWinegrowers have already noticed the effects of climate change, and they are changing some agricultural practices. Drip irrigation was the most frequently mentioned option to minimize these effects. The map of knowledge identifies the main nodes in the information flow. Results also highlight different approaches to climate change and interesting nuances in the maps of knowledge among winegrowers with and without winery.Research limitations/implicationsThis paper is focused on the Madrid region, a territory that is still consolidating its wine sector at the economic and marketing levels. We understand that regions with more consolidated or stronger sectors involve maps of knowledge more complex than that obtained in this study.Practical implicationsShowing the nodes of knowledge, as well as the weaknesses and strengths of the information circuit in the wine sector in the Madrid region, is very relevant to developing strategies aimed at supporting innovation in this sector. From a practical point of view, strategies for knowledge generation and circulation are only one part of the innovation process – policies for financial and technical support are key complementary measures.Social implicationsIdentification of key agents in the innovation process in the wine sector is essential to foster innovation processes. Ultimately, this will lead to more efficient adaptation to new challenges in the sector.Originality/valueThe Agriculture Knowledge and Innovation Systems (AKIS) approach has a consolidated theoretical framework that pays great attention to knowledge flows, but specific studies are needed to capture the reality of AKIS by sector and by region.

The observer observed: Ethnographic discomforts and (a)symmetrical relationships in a digital ethnography

This paper engages in a reflection about the use of social media to carry out fieldwork online for contemporary ethnography. The reflection is based on the ethnographer’s experiences of discomforts and affects in digital fieldwork, in which she used her own social media accounts to interact with the interlocutors recruited for the project as a means to create reciprocity with them. This article uses these discomforts as generators of ethical, epistemological and political reflections by discussing the positionality of the ethnographer in a digital fieldwork. Firstly, it delves into the potential of these discomforts as a reflective tool for knowledge generation. It also reflects on the power dynamics consequence of the position that the anthropologist occupies in the field. Finally, the article initiates a political reflection on academic life when extensive exposure to fieldwork breaks down the boundaries between work and personal life.

Exploring the Complexity of Adaptive Teaching Expertise within Knowledge Generation Environments

The shift towards Next Generation Science Standards represents a paradigmatic change in teaching, transitioning from knowledge transmission to knowledge generation approaches. This reform underscores the complexity of teaching expertise, extending beyond mere knowledge to require a profound comprehension of generative learning environments. In this study, we explore Adaptive Teaching Expertise (AdTex), defining it as a teacher’s capacity characterized by fluidity and reflexiveness in teaching dynamics, rather than just flexibility. Through a complexity framing approach, we delineate three layers of AdTex: the visible actions of teachers, the semi-visible use of epistemic tools such as language, dialogue, and argument, and the tacit orientations towards learning that encompass epistemological, ontological, and axiological dimensions. Our research primarily investigates the intricate relationship between the epistemic tool and orientation layers. Our findings highlight the significance of an interconnected understanding and the impact of philosophical orientations on adaptive teaching practices. A notable contribution of this study is the development of a framework that articulates the belief and knowledge systems crucial for fostering generative learning environments, alongside the introduction of complexity maps to illustrate the interplay among these subsystems.

Building to learn: Information technology innovations to enable rapid pragmatic evaluation in a learning health system.

Learning health systems (LHSs) iteratively generate evidence that can be implemented into practice to improve care and produce generalizable knowledge. Pragmatic clinical trials fit well within LHSs as they combine real-world data and experiences with a degree of methodological rigor which supports generalizability. We established a pragmatic clinical trial unit ("RapidEval") to support the development of an LHS. To further advance the field of LHS, we sought to further characterize the role of health information technology (HIT), including innovative solutions and challenges that occur, to improve LHS project delivery. During the period from December 2021 to February 2023, eight projects were selected out of 51 applications to the RapidEval program, of which five were implemented, one is currently in pilot testing, and two are in planning. We evaluated pre-study planning, implementation, analysis, and study closure approaches across all RapidEval initiatives to summarize approaches across studies and identify key innovations and learnings by gathering data from study investigators, quality staff, and IT staff, as well as RapidEval staff and leadership. Implementation approaches spanned a range of HIT capabilities including interruptive alerts, clinical decision support integrated into order systems, patient navigators, embedded micro-education, targeted outpatient hand-off documentation, and patient communication. Study approaches include pre-post with time-concordant controls (1), randomized stepped-wedge (1), cluster randomized across providers (1) and location (3), and simple patient level randomization (2). Study selection, design, deployment, data collection, and analysis required close collaboration between data analysts, informaticists, and the RapidEval team.

Caring for Scholarship in Transition

This editorial briefly reflects on the idea of transition—a theme that cuts across energy systems, migration, and education, to name a few—and is likely familiar to many readers. In this issue, we focus on transition within the context of scholarly publishing; maintenance and repair studies; and experimental methods. We reflect on how the Issue’s thematic collection, “Maintenance & its Knowledges” guest edited by Jérôme Denis, Daniel Florentin, and David Pontille, raises ontological questions about the systems we continuously repair and maintain. We also introduce two original research articles, both of which discuss projects that used collaborative ethnography to disrupt normative modes of knowledge generation. Many of the articles in this issue engage the theme of socio-technical change, a classic STS topic that becomes a point of intervention, if at varying scales and using contrasting approaches. Collectively, the Issue can be read as a theory-method buffet that offers tools for working with and through transition.

Educational models and technologies in the era of digital transformation

At present digital transformation of the society faces the problem of a shortage of a highly qualified personnel - specialists in the field of the latest information technologies, intellectual scientists with a high-quality fundamental education. The solution to this problem lies in the introduction of new educational models and technologies. The purpose of the research is to study the strategy for the formation of a new conceptual scheme of modern education. It has been emphasized that two leading educational models are currently being successfully implemented and complement each other: the classical model of «completed» education with the basic principle of «education once and for life», covering the need of the society for the generation and dissemination of knowledge, as well as the storage of cultural ideals, and the second model, called the «lifelong education model», which meets the principle of «lifelong education», corresponding to the rapid changes in technology and the nature of digital transformations of the society. The article pays special attention to new educational technologies such as: 1) analytical technologies, which are based on the analysis of big data from educational institutions in real time; 2) adaptive (or intellectual) learning, which is based on the principles of personalization of learning and building an individual educational trajectory; 3) Smart education, using learning tools in an interactive educational environment with elements of distance and mobile education. The methodological basis of the research is a systematic approach and general methods of scientific knowledge, such as analysis, synthesis, generalization. The relevance of the topic is associated with increased attention to the problem of matching the quality of the Russian education with the promising tasks of the digital transformation of the Russian society. Key conclusion. A high professional level of the personnel reserve, which will be able to practically implement state digitalization programs, is possible and is associated with the construction of a new concept of modern education.

Deductive Care Methodology: Describing and testing modes of care research

ObjectiveDefine the modes of procedure of the Deductive Care Methodology (DCM) in the generation of knowledge about person's health care. MethodologyDesign and test of the DCM modes based on three phases: mapping of the DCM, generation of models from this methodology and testing of the models through studies in a clinical context. ResultsThe DCM presents five levels of abstraction with three modes broken down to 16 types. The modes are: Philosophical Mode to conceptualize and obtain generalities about reality, Mathematical Mode to operate with generalities, and Physical Mode to operationally verify, validating the results and the predictive capacity of the model. This MDC allows the creation of three models: Knowledge Model about Person Care, an ontology of care, Vulnerability Model about the person and Taxonomic Triangulation Model for knowledge management. All models generate products for computational knowledge management. In addition, the models are applied in teaching and generate research with more than a hundred participations in conferences and journals, of which five impact publications (from 2008 to 2022) classified in the categories of Nursing and Informatics are analysed. ConclusionsThe DCM collects prior knowledge to work with certainties, evidence and applying inferences that do not depend on the number of cases or inductive designs. This research presents a formal structure of the DCM with an interdisciplinary orientation between Health Sciences and Computer Sciences.

The Relationship Between Organizational Learning and Innovation: A Case Study of Learning Organizations

This article examines how organisational learning and innovation interact. Innovation, which boosts productivity and competitiveness, requires workplace learning. This study examines the interactions of these two main variables holistically. 300 employees from 100 enterprises and 100 stakeholders with close relationships to these organisations are polled, interviewed, and their corporate records evaluated in a mixed-methods study. We assess how well these companies’ organisational learning practices foster innovation. This study’s findings on organisational learning and creativity illuminate various aspects, including knowledge generation, sharing, application, and reflection. The study also examines organisational barriers to innovation and learning. This study analyses employee and stakeholder perspectives to better understand organisational learning and innovation. These findings affect the development of learning, innovation, and corporate performance approaches. This study helps companies capitalise on the synergy between organisational learning and innovation to grow and compete.

Industrial cluster and knowledge creation: a bibliometric analysis and literature review

The interaction between the development of industrial clusters and knowledge generation has become a fundamental element in driving innovation systems. Examining industrial clusters in knowledge creation terms is interesting for policymakers, investors, businesses and academia. This article provides a thematic and intellectual overview of the existing scientific literature on industrial clusters, their evolution and their relation with knowledge creation. The aim is to identify the scientific literature foundations in this area, different intellectual, conceptual and social structures, and trends in the research field. The study is a bibliometric analysis, whose results show that researchers’ topics of interest have considerably evolved over the last four decades. Initially, most studies focus on policies for efficient clusters management and later, starting in the 2010s, they evolve towards the importance of the networks of resources and knowledge that clusters provide, and how to promote learning and innovation as a key activity for economic development.