Effective Knowledge Transfer Research Articles

Research on the knowledge transfer mechanism of digital platform in the digital innovation ecosystem: an improved model of SIR embedded in symbiosis theory

PurposeIn the digital economy era, digital platforms are vital infrastructure for innovation subjects to perform digital innovation activities. Achieving efficient and smooth knowledge transfer between innovation subjects through digital platforms has become a novel research subject. This study aims to examine the knowledge transfer mechanism of digital platforms in the digital innovation ecosystem through modeling and simulation to offer a theoretical basis for digital innovation subjects to acquire digital value through knowledge-sharing and thus augment their competitive advantage.Design/methodology/approachThis study explores the optimal symbiotic interaction rate between different users based on the classic susceptible-infected-removed (SIR) model. Additionally, it constructs a knowledge transfer mechanism model for digital platforms in the digital innovation ecosystem by combining the theories of communication dynamics and symbiosis. Finally, Matrix Laboratory (MATLAB) software is used for the model and numerical simulation.FindingsThe results demonstrate that (1) the evolutionary path of the symbiotic model is key to digital platforms' knowledge transfer in the digital innovation ecosystem. In the symbiotic model, the knowledge transfer path of digital platforms is “independent symbiosis—biased symbiosis (user benefit)—reciprocal symbiosis,” aligning with the overall interests of the digital innovation ecosystem. (2) Digital platforms' knowledge transfer effects within the digital innovation ecosystem show significant differences. The most effective knowledge transfer model for digital platforms is reciprocal symbiosis, whereas the least effective is parochial symbiosis (platform benefit). (3) The symbiotic rate has a significant positive impact on the evolutionary dynamics of knowledge transfer on digital platforms, especially in the reciprocal symbiosis model.Originality/valueThis study's results aid digital innovators in achieving efficient knowledge transfer through digital platforms and identify how symbiotic relationships affect the knowledge transfer process across the ecosystem. Accordingly, the authors propose targeted recommendations to promote the efficiency of knowledge transfer on digital platforms.

Tensor representation-based transferability analytics and selective transfer learning of prognostic knowledge for remaining useful life prediction across machines

In recent years, deep transfer learning techniques have been successfully applied to solve RUL prediction across different working conditions. However, for RUL prediction across different machines in which the data distribution and fault evolution characteristics vary largely, the extraction and transition of prognostic knowledge become more challenging. Even if fault mode information can assist in the knowledge transfer, model bias will inevitably exist on the target machine with mixed or unknown faults. To address this issue from a transferability perspective, this paper proposes a novel selective transfer learning approach for RUL prediction across machines. First, the paper utilizes the tensor representation to construct the meta-degradation trend of each fault mode and evaluates the transferability of source domain data from fault mode and degradation characteristics through a new cross-machine transfer degree indicator (M-TDI). Second, a Long Short-Term Memory (LSTM)-based selective transfer strategy is proposed using the M-TDIs. The paper designs a training algorithm with an alternating optimization scheme to seek the optimal tensor decomposition and knowledge transfer effect. Theoretical analysis proves that the proposed approach significantly reduces the upper bound of prediction error. Furthermore, experimental results on three benchmark datasets prove the effectiveness of the proposed approach.

What Is Next? The Longer-Term Managerial Challenges following COVID-19

Existing research has highlighted the immediate challenges posed by the enforced work-from-home arrangements during the COVID-19 pandemic, particularly in the effective transfer and retention of knowledge, especially tacit knowledge, due to the reduced opportunities for face-to-face interactions. This study seeks to delve into the enduring concerns and obstacles faced by business managers as they navigate a return to normalcy in their operations. We conducted a grounded approach study between September and December 2021, involving semi-structured interviews with thirteen managers from eight business firms in Hong Kong who had experience supervising and undergoing work-from-home arrangements during the COVID-19 pandemic. Our research uncovered three layers of challenges. The first layer, echoing the existing literature, revolves around the inefficiencies in knowledge sharing stemming from the absence of physical interactions among colleagues. The second layer addresses the difficulties related to the socialization, performance, and retention of newcomers who joined the workforce during the pandemic. The third layer represents a longer-term challenge, encompassing skill and talent shortages attributed to the lack of intergenerational knowledge transfer. While our study acknowledges its limitations regarding representativeness and the absence of extensive quantitative evidence often associated with exploratory research, it nonetheless offers valuable insights for researchers and managers alike. These insights illuminate the imminent challenges that will surface as we navigate the recovery from the COVID-19 pandemic. Consequently, this paper holds significance for business sustainability by directing managerial attention towards addressing the issues of skill and talent shortages that may hinder business performance or even its survival in the post-COVID-19 era.

Cultural Intelligence, Firm Capabilities, and Performance: The Case of German Subsidiaries in Malaysia

The extant literature shows that firm capabilities such as knowledge transfer and innovation are highly related to firm performance. How these relationships express themselves for foreign-based firms operating in global environments is less understood. The objectives of this study are as follows: (1) to examine the differential effects of knowledge transfer and innovation on the performance of German-based companies operating in Malaysia; (2) to determine if these relationships and effects operate via an “intervening variable” or mediator, in this case, competitive advantage; and (3) to determine if another latent variable (cultural intelligence) has an altering or “moderating” influence on the effects of innovation and knowledge transfer on competitive advantage. Analyses of the causal relationships are tested using a sample of 475 respondents working in German subsidiaries in Malaysia and applying a structural equation model. The empirical findings indicate that innovation and knowledge transfer are positively and significantly related to German companies’ overall performance, that these effects are mediated by competitive advantage, and that cultural intelligence interacts with innovation and knowledge transfer to affect the strength of the relationships between innovation (knowledge transfer) and competitive advantage.

Cognitive Psychology: As essential & integrated part of medical education w.s.r. to Yukti Pramana in Ayurveda

Growth of any knowledge base depends on a dynamic continuum of a tripod of creating new knowledge through research, effective transfer of knowledge through proactive education and utilization of the knowledge with good professional practice. A set of knowledge system is likely to be relative to time & place setting which facilitates its need based development. But though Ayurvedic education system is one of the recognized age old education systems but still neither developed nor advanced alike other education system across the globe. Due to lack of maintenance of such dynamic continuum in current education system of ancient system of Indian medicine, the knowledge bases of Ayurveda becomes stagnant, static & gradually pass into merely history &may not remain dynamic & useful. Need of dynamism and activism through incorporation of cognitive psychology in education curriculum and enhancement of reasoning skill followed by critical thinking in the mind of medical professionals is quite necessary in the field of Ayurveda to make it up to date and develop it further in tune with the changing needs of the society today. Keeping this in view an effort has been made by the author to explore the concept of Yukti as key element of cognitive psychology make its relevant application in the field of current teaching & education and practice of Ayurved worldwide.

Examining the Setting of Significant Learning Events during the Engineering School-to-Work Transition

The school-to-work transition is a critical time for engineers that involves rapid learning across multiple fronts, but relatively little is known about the setting (i.e., how, where, and with whom) of significant learning experiences during this time. The purpose of the study is to examine the setting of significant learning events for recent engineering graduates. We used a multi-case study in which 12 recent engineering graduates responded to weekly reflective journal prompts for the first twelve weeks of their transition from school to work. Participants described significant learning events through a series of open-ended questions. We used both deductive and inductive coding to identify the setting of the event in terms of how, where, and with whom engineers engaged in learning at work. The findings highlight the emergent, social nature of workplace learning and point to critical differences across school and work. To better prepare students for professional practice, engineering educators should consider how they might create learning environments that promote effective transfer of knowledge and skills.

Role of FDI in Export Growth: A Comprehensive Review

Particularly in the era of globalization, foreign direct investment (FDI) has emerged as a critical engine of economic growth and development for many nations. This study intends to investigate how FDI supports export growth in both developed and developing countries. It explores how FDI influences export performance, including knowledge transfer, market access, production capacity, and spillover effects. The study covers the body of research, empirical studies, and case studies to analyze the link between FDI and export growth. Governments and investors may learn crucial lessons about improving FDI regulations and promoting global commerce by putting light on this issue. Keywords:- FDI, Export growth, Economic growth, development, etc.

Artificial Intelligence Generative Tools and Conceptual Knowledge in Problem Solving in Chemistry

In recent years, artificial intelligence (AI) has emerged as a valuable resource for teaching and learning, and it has also shown promise as a tool to help solve problems. A tool that has gained attention in education is ChatGPT, which supports teaching and learning through AI. This research investigates the difficulties faced by ChatGPT in comprehending and responding to chemistry problems pertaining to the topic of Introduction to Material Science. By employing the theoretical framework proposed by Holme et al., encompassing categories such as transfer, depth, predict/explain, problem solving, and translate, we evaluate ChatGPT’s conceptual understanding difficulties. We presented ChatGPT with a set of thirty chemistry problems within the Introduction to Material Science domain and tasked it with generating solutions. Our findings indicated that ChatGPT encountered significant conceptual knowledge difficulties across various categories, with a notable emphasis on representations and depth, where difficulties in representations hindered effective knowledge transfer.

Creative Kampong and the Promotion of Sustainable Development Knowledge in Urban Grassroots: The Case of Surakarta

As the global trend is going into circular economy paradigm. The trend encourages business entities to focus on the production of durable, efficient, and reusable commodities to promote sustainable development. On the demand side, the market also gradually adjusts its preferences to commodities with sustainability values. With limited scale of capital, networks, and technology, Small and Medium Enterprises (SMEs) are usually lagged behind in catching new knowledge, especially those exchanged internationally. Sustainability, which is as an important knowledge under the circular economy trend, has caused serious problem for SMEs because those unable to catch the knowledge will be incompetent to serve the new market preferences and vulnerable to survive. Urban planning practices introduce the business district as one of spatial concepts to mediate the problem. It encourages enterprises with business linkages to locate next to each other and share activities and facilities. In Indonesia, the creative kampong is a local translation of business district concept, which emerges as a national hype. This research aims at studying the empirical case of the creative kampong and its knowledge transfer effectiveness for SMEs. By using the logic of Actor Network Theory (ANT) and focusing on the case study of four creative kampongs in Surakarta, we unravel the level of understanding of SMEs towards sustainability. Our data are based on stakeholder interviews and documentary reviews. The findings reveal that the majority of SMEs were understand of sustainability, and the creative kampong provided a significant contribution to ease the transfer knowledge process.

Strategy for learning microcontroller programming—a graphical or a textual start?

Microcontroller programming competencies contribute to the sustainable employability of engineering graduates of both higher and secondary education. To develop the required programming skills, one of the challenges for educators is to determine which programming environments should be implemented in introductory programming courses. Conceptually, graphical (e.g. iconic or diagrammatic) environments appear to be very different from textual environments. Our study focused on a programming course in a mechatronics vocational training programme at the secondary school level in Slovenia. To investigate the expectations of potential employers towards our graduates, we surveyed local companies. Out of 104 respondents, 90 (86.5%) expected graduates to be able to use various programming environments, including upcoming ones. In our study, we divided 114 students into two groups of equal prior knowledge. Group A started the course with a textual programming environment and switched to a graphical one in the second sequence, while group B followed a reverse sequence. Group A achieved better test results after both sequences. Knowledge transfer, as measured by normalised learning gains, was also in favour of group A. We concluded that it is more efficient to start with the textual environment and then continue with the graphical environment. The results of this study open up some challenges for further research to investigate the effectiveness of introductory programming courses based on programmable electronics with students of similar ages. Further research should consider the specific requirements of the different courses but should also the effectiveness of knowledge transfer between different programming environments.

Proposing Business Growth of PT. DECRA Group Indonesia through Knowledge Management

The informatics industry market has shown promising growth in the post-COVID era, presenting certification companies with lucrative business opportunities. The informatics market’s GDP growth rate ranks fourth among other fastest-growing industries in Indonesia, demonstrating a 7.19% increase from 2022 to 2023. PT. DECRA Group Indonesia, a certification company, have intention to penetrate a new market which is IT Industry. Knowledge plays a crucial role in the certification process, especially ISO auditors are required extensive knowledge across various business types. Therefore, this research focuses on exploring the role of knowledge management in supporting the growth of PT. DECRA Group Indonesia. The study aims to investigate how knowledge management approaches can enable DECRA Group Indonesia to improve their resources and capitalize on the IT market. The data collection method in this research involves gathering qualitative data through interviews with the business owner and HR manager as the primary sources. These interviews aim to gain an in-depth understanding of their perspectives on the company’s business growth. Additionally, secondary data sources such as the company’s website, competence review forms, and lists of employee competencies will be utilized to supplement the research. The analysis employs Goal-Analysis approach using Fishbone Diagram model to identify what key areas in achieving company’s goals. Building upon the analysis, KM Framework Table is constructed based on the People, Process, and Technology for each improvement that need to be made. Then SECI Model, derived from the KM Framework Table, guides the implementation of knowledge management practices, facilitating the conversion of tacit knowledge into explicit knowledge to foster innovation and organizational learning. Furthermore, to ensure successful implementation in SECI model, an Integrated Learning Cycle model is developed, which takes into consideration the communication channels and learning processes that are crucial for the effective knowledge transfer and acquisition. To facilitate the practical implementation of knowledge management strategies, a KM Roadmap is developed by providing a simple-structured approach for DECRA in aligning its people, processes, and technology considerations to support knowledge management initiatives. In conclusion, this research emphasizes the critical role of knowledge management in driving business growth for PT. DECRA Group Indonesia, enabling the company to enhance its internal operations, develop competencies, capitalize IT market successfully, and leverage knowledge as a strategic asset for sustained innovation and competitive advantage.

THE USE OF ARTIFICIAL INTELLIGENCE AND NEURAL NETWORKS IN THE EDUCATIONAL PROCESS IN PROFESSIONAL DISCIPLINES BY STUDENTS MAJORING IN PROFESSIONAL EDUCATION (DIGITAL TECHNOLOGIES)

This article examines the possibilities and prospects of using artificial intelligence and neural networks in student education. The use of these technologies can have a positive impact on the quality of education and provide more effective knowledge transfer. The authors of the article explore the possibilities of using neural networks in the study of professional disciplines by students of the specialty «Vocational Education (Digital Technologies)». Among the advantages of using artificial intelligence and neural networks in education are the optimization of the learning process, adaptive and individualized learning, increased motivation, and automatic assessment of knowledge and skills. Additionally, the possibility of using neural networks for data analysis is considered, which will allow students to improve the quality of research and increase the accuracy of results. Moreover, artificial intelligence systems can help ensure more effective remote learning. Furthermore, artificial intelligence can be applied to create virtual simulators and trainers, allowing students to acquire practical skills and experience without risking experimentation on real objects. For instance, this can be useful for students who study computer repair and modernization, where practical experience and safe and efficient equipment handling are essential.
 Despite the advantages of using these technologies, such as personalized learning and increased efficiency, potential drawbacks should also be considered. Possible drawbacks include low-quality source data, potential errors in the neural network's operation, and the possibility of ethical problems. To minimize the negative impact of these drawbacks, it is necessary to use technologies that consider their limitations and risks, as well as to constantly improve the technical base and increase the qualifications of teachers and students. Therefore, the use of artificial intelligence and neural networks in education is a promising and powerful tool that can improve the quality of education, make it more efficient and personalized, and provide students with the skills and knowledge they need to succeed in the digital age.

Coevolutionary Framework for Generalized Multimodal Multi-Objective Optimization

Most multimodal multi-objective evolutionary algorithms (MMEAs) aim to find all global Pareto optimal sets (PSs) for a multimodal multi-objective optimization problem (MMOP). However, in real-world problems, decision makers (DMs) may be also interested in local PSs. Also, searching for both global and local PSs is more general in view of dealing with MMOPs, which can be seen as a generalized MMOP. In addition, the state-of-the-art MMEAs exhibit poor convergence on high-dimension MMOPs. To address the above two issues, in this study, a novel coevolutionary framework termed CoMMEA for multimodal multi-objective optimization is proposed to better obtain both global and local PSs, and simultaneously, to improve the convergence performance in dealing with high-dimension MMOPs. Specifically, the CoMMEA introduces two archives to the search process, and coevolves them simultaneously through effective knowledge transfer. The convergence archive assists the CoMMEA to quickly approaching the Pareto optimal front (PF). The knowledge of the converged solutions is then transferred to the diversity archive which utilizes the local convergence indicator and the $\epsilon$-dominance-based method to obtain global and local PSs effectively. Experimental results show that CoMMEA is competitive compared to seven state-of-the-art MMEAs on fifty-four complex MMOPs.

Generative Multiform Bayesian Optimization.

Many real-world problems, such as airfoil design, involve optimizing a black-box expensive objective function over complex-structured input space (e.g., discrete space or non-Euclidean space). By mapping the complex-structured input space into a latent space of dozens of variables, a two-stage procedure labeled as generative model-based optimization (GMO), in this article, shows promise in solving such problems. However, the latent dimension of GMO is hard to determine, which may trigger the conflicting issue between desirable solution accuracy and convergence rate. To address the above issue, we propose a multiform GMO approach, namely, generative multiform optimization (GMFoO), which conducts optimization over multiple latent spaces simultaneously to complement each other. More specifically, we devise a generative model which promotes a positive correlation between latent spaces to facilitate effective knowledge transfer in GMFoO. And furthermore, by using Bayesian optimization (BO) as the optimizer, we propose two strategies to exchange information between these latent spaces continuously. Experimental results are presented on airfoil and corbel design problems and an area maximization problem as well to demonstrate that our proposed GMFoO converges to better designs on a limited computational budget.

Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging.

The collection and curation of large-scale medical datasets from multiple institutions is essential for training accurate deep learning models, but privacy concerns often hinder data sharing. Federated learning (FL) is a promising solution that enables privacy-preserving collaborative learning among different institutions, but it generally suffers from performance deterioration due to heterogeneous data distributions and a lack of quality labeled data. In this paper, we present a robust and label-efficient self-supervised FL framework for medical image analysis. Our method introduces a novel Transformer-based self-supervised pre-training paradigm that pre-trains models directly on decentralized target task datasets using masked image modeling, to facilitate more robust representation learning on heterogeneous data and effective knowledge transfer to downstream models. Extensive empirical results on simulated and real-world medical imaging non-IID federated datasets show that masked image modeling with Transformers significantly improves the robustness of models against various degrees of data heterogeneity. Notably, under severe data heterogeneity, our method, without relying on any additional pre-training data, achieves an improvement of 5.06%, 1.53% and 4.58% in test accuracy on retinal, dermatology and chest X-ray classification compared to the supervised baseline with ImageNet pre-training. In addition, we show that our federated self-supervised pre-training methods yield models that generalize better to out-of-distribution data and perform more effectively when fine-tuning with limited labeled data, compared to existing FL algorithms. The code is available at https://github.com/rui-yan/SSL-FL.

The effect of internal knowledge transfer on the relationship between personality traits and employee service performance: a multilevel model

PurposeInternal knowledge transfer is crucial for firms to improve their employees’ abilities and improve their work performance. However, there is still a gap in the knowledge management field regarding whether internal knowledge transfer can leverage employee personality traits and service performance in service-oriented organizations. To address this gap, this study aims to validate a multilevel model of the mediating (i.e. internal knowledge transfer as a mediator) and moderating (i.e. task interdependence as a moderator) mechanisms underlying personality traits and employee service performance.Design/methodology/approachMultilevel structural equation modeling was applied for model validation using an original data set from 45 team leaders and 333 employees working in Chinese hotels.FindingsInternal knowledge transfer mediated the link between extraversion and employee service performance and the link between openness to experience and employee service performance. Task interdependence played a moderating role that strengthened both the impacts of extraversion and openness to experience on internal knowledge transfer.Originality/valueThrough the use of an original data set, this study advances the knowledge management discipline by investigating the mediating impact of internal knowledge transfer between personality traits and employee service performance and revealing the moderating impact of task interdependence that underlies the links between personality traits and internal knowledge transfer.

ENHANCING PERFORMANCE THROUGH TOTAL QUALITY MANAGEMENT: UNVEILING THE MEDIATING INFLUENCE OF KNOWLEDGE TRANSFER

In today's dynamic and competitive business environment, the organizations strive to attain superior performance by executing effective management strategies. One such strategy that has gained significant attention is Total Quality Management (TQM). This study aims to delve into the relationship between TQM and organizational performance, with a specific focus on the mediating effects of knowledge transfer. Drawing on comprehensive review of literature and empirical analysis, this research investigates the impact of TQM practices upon organizational performance, considering the crucial role played by knowledge transfer mechanisms. The study employs quantitative research design, collecting data from a manufacturing SME’s operating in Lahore, Pakistan. SEM is used to analyze data and test proposed hypotheses. The findings shed light on the significant positive relationship amid TQM & organizational performance, highlighting vital role of knowledge transfer as a mediating mechanism. The organizations that aim to improve performance should focus not only on implementing TQM practices but on establishing effective knowledge transfer mechanisms.

Measuring efficiency and effectiveness of knowledge transfer in e-learning

With e-learning rapidly gaining popularity, evaluating its effectiveness and efficiency has become a challenge in public education, the public sector, and the corporate sector. Measuring knowledge transfer is crucial in any learning process, but e-learning lacks validated methods for this. Here we examine ways to evaluate that particularly in case of e-learning, conducting a literature review to assess available measurement solutions, developing an evaluation method for knowledge transfer, and validating the method. Using logged data from e-courses, it is possible to assess the knowledge transfer in e-learning. We describe a novel method for classifying effectiveness and efficiency with measured values and measurement instruments. The new measurement method was aligned with a data set of an existing learning management system, and the effectiveness and efficiency of knowledge transfer was analysed using quantitative means, including descriptive statistics, regression modelling, and cluster analysis based on a specific e-learning course. This newly elaborated and validated knowledge transfer measurement technique could be a useful tool for anyone wanting to evaluate e-learning courses and can also serve as a baseline for academics to further develop or implement it on larger empirical datasets.

Differentially private knowledge transfer for federated learning

Extracting useful knowledge from big data is important for machine learning. When data is privacy-sensitive and cannot be directly collected, federated learning is a promising option that extracts knowledge from decentralized data by learning and exchanging model parameters, rather than raw data. However, model parameters may encode not only non-private knowledge but also private information of local data, thereby transferring knowledge via model parameters is not privacy-secure. Here, we present a knowledge transfer method named PrivateKT, which uses actively selected small public data to transfer high-quality knowledge in federated learning with privacy guarantees. We verify PrivateKT on three different datasets, and results show that PrivateKT can maximally reduce 84% of the performance gap between centralized learning and existing federated learning methods under strict differential privacy restrictions. PrivateKT provides a potential direction to effective and privacy-preserving knowledge transfer in machine intelligent systems.

THE INFLUENCE OF KNOWLEDGE ABOUT HEURISTIC DECISIONS AND COGNITIVE BIASES IN ENGINEERING STUDENTS AND JUNIOR ENGINEERS: CASE STUDIES FROM GERMANY AND THAILAND

AbstractUnderstanding heuristics and cognitive biases might lessen their impact since they can cause decision errors. From the responses to the survey for case studies, this study aims to investigate the fundamental understanding of heuristics and cognitive biases in engineering students and junior engineers from Germany and Thailand. The results indicated that the majority of participants knew very little about them. In a brief lecture, only few students from Germany knew about them. They had less of an impact on students who already knew them than on those who did not. However, engineers were unaware of them and are able to limit their effects. That implies that they can manage cognitive biases' effects without being aware of them. Therefore, experience is another crucial element in lessening the impact of biases. The behavior can also impact how much cognitive biases influence decision-making. Culture and environment affect the way of thinking. Although the finding suggests that knowledge is not the primary element in decreasing the impact of heuristics and cognitive biases, it is still vital to explain and further study the level of knowledge and the effectiveness of knowledge transfer to determine how they may have an impact.