Knowledge Transfer Mechanisms Research Articles

Challenges and the Opportunities of Community-Based Innovation in Indonesia

The research examined the challenges and opportunities of community-based innovation in Indonesia by conducting a case study in the Petani Muda Keren (PMK) Bali Community, a pilot community for innovative farming development in Bali province. In addition, the research also provided a knowledge transfer mechanism in the community to explore how innovation diffusion happens in the community. As a group of people with a common interest and mission, a farmer’s community could be an effective place for innovation diffusion by offering emotional support to handle psychosocial issues. Moreover, it could construct its members’ perceptions of technology through social shaping processes. A qualitative case study was applied in the research to explore community-based innovation in Indonesia’s agriculture that highlighted Petani Muda Keren Bali Community. Through observation and in-depth interviews with the community founder and twenty farmers from five regions in Bali, the current research indicates challenges and opportunities of community-based innovation in Indonesia related to several issues, including innovation rate and farmers’ readiness, the perception gap between young and elderly farmer, the inconsistency of young members, and government support and policies to the community.

Investigating the Quality of Knowledge Transfer

Dominant research streams in management conclude that knowledge transfer between managers and associated patient groups is accomplished through compatible instructions and/or socially assembled practices. Underlying these views is the belief that any texts and practices carry with them the codes necessary for their decoding and, therefore, enable an unproblematic knowledge transfer to naturally occur. The research asked if this relationship could be improved from both a personal and organisational perspective by developing a better understanding of the various knowledge transfer mechanisms at work. These findings show that in an organisational context, different perspectives to knowledge must be comprehensively understood before any technique to reduce transfer anomalies is introduced within an environment.

Intergenerational Tsunami Knowledge Transfer Sixteen Years After the Tsunami in Aceh, Indonesia

The 2004 earthquake and tsunami left a lot of sorrow in Indonesia, especially in Aceh. It was reported that 127,720 people died and 93,285 people were missing after the tsunami. The total damage and losses reached 41.4 trillion rupiah. One of the reasons for the high number of victims in Aceh was the lack of tsunami information and knowledge transfer. The purpose of this study was to analyze how tsunami knowledge transfer occurs across generations and the extent to which people possess basic knowledge about tsunamis. This study used a mixed-methods design. Surveys and interviews were used as the data collection techniques. There were 300 respondents selected with several criteria, spread from three sub-districts. These sub-districts were chosen based on the level of regional risk from low to high, namely Baiturrahman, Syiah Kuala, and Meuraxa sub-districts. In each sub-district, 100 people were included. The data were analyzed using descriptive methods. The results showed that there was a difference in the mechanism of tsunami knowledge transfer between pre-tsunami and post-tsunami generations. The pre-tsunami generation transferred the tsunami knowledge bidirectionally to contemporaries and the younger generation. The post-tsunami generation transferred the knowledge in one direction, contemporaneously. Lastly, factors that influenced tsunami knowledge transfer were: knowledge, experience, work, and the media. Keywords: knowledge transfer, tsunami
 knowledge transfer, mechanism, knowledge transfer mechanism

Growth Path of Industrial Clusters Embedded in Global Value Chains From the Perspective of Knowledge Transfer

How local industrial clusters realize industrial upgrading in the development process of embedded global value chain is the central topic. To explore how industrial clusters achieve the enhancement of their innovation capability and value chains when they are embedded in the global value chain, from the perspective of knowledge transfer, three fuzzy game models of knowledge transfer paths were constructed, and the model of the realization mechanism of knowledge transfer and its stability condition was analyzed, which make clear the path of cluster growth under different embedding modes. Results show that the knowledge transformation ability of local industrial clusters is the determining factor that the knowledge transfer can smoothly achieve and become stable. The conclusion also shows the feasibility of the cross-sectional growth of industrial clusters by actively embedding the global value chain and acquiring external knowledge transfer if the industrial clusters want to enhance their technology accumulation, their innovation ability, and their position in the global value chain.

Multi-objective multitasking optimization based on positive knowledge transfer mechanism

Multi-objective multitasking optimization (MTO) is an emerging research topic in the field of evolutionary computation, which can solve multiple optimization tasks simultaneously and improve the convergence speed of each task. Recently, it has been demonstrated that the useful knowledge is always hidden in valuable solutions. When solving MTO problems, the core issue is how to select valuable solutions from the source task to help the target task. In this study, a multi-objective evolutionary multitasking algorithm based on positive knowledge transfer mechanism is proposed. Specifically, a cheap surrogate model is introduced to evaluate the quality of the solutions, which can find valuable solutions. Moreover, a diversity maintenance method is designed to maintain the diversity of solutions in each task. Finally, the selection strategy of transferred solutions is put forward to find valuable solutions with good diversity, which can improve the efficiency of positive knowledge transfer. Experiments on two MTO test suites and a real-world case demonstrate that the proposed algorithm is effective and competitive.

Boundary Conditions of Subsidiary Reverse Knowledge Transfer Benefiting the MNE Parent

The subsidiary reverse knowledge transfer (RKT) has recently drawn much attention in the IB research. Despite prior efforts on exploring the determinants of the subsidiary RKT from diverse perspectives, the subsidiary RKT benefit to the MNE parent in contingency with heterogeneity of subsidiary entrepreneurial initiatives, the MNE parent’s discretion over the subsidiary resource use, and the knowledge transfer mechanism are rarely examined in a holistic approach. Drawing on the organizational learning perspective and resource dependence theory to develop our hypotheses, and testing them with a unique sample of 2277 observations of 1831 Taiwan MNEs collected from 2013 to 2017, we found that (1) the RKT both of subsidiary exploitative and explorative initiatives are beneficiary to the MNE parent, and the benefit derived from the exploitative initiative is stronger than the explorative one; (2) the knowledge transfer conduit of bureaucratic routine is more detrimental to the RKT benefit of subsidiary explorative initiative; and (3) the MNE parent’s higher resource discretion enhances the RKT benefit of subsidiary explorative initiative. Our findings provide academic contributions to the IB literature and managerial implications for managing the subsidiary RKT.

The Mediating Role of Tacit Knowledgel in Knolwedge Recontexualisation by Returnee Entrepreneurs

Recontextualising knowledge is the key to effective knowledge transfer across different business contexts. While knowledge recontextualization has been explored in intra-firm knowledge transfer within multinational enterprises, few studies have extended the concept of knowledge recontextualization to returnee mobility - an effective international knowledge transfer mechanism by returnee entrepreneurs. Using our own surveyed data of 91 Vietnamese returnee entrepreneurs, this study examines under what institutional conditions and individual characteristics returnee entrepreneurs can successfully recontextualize brought back international knowledge and enhance firm performance when operating in their home country. We found that the transfer of tacit knowledge plays an important mediating role to achieve the effective knowledge recontextualization. Keeping home embeddedness while living aboard is critical to help returnees fit back to home country while too much or too little host embeddedness can harm their knowledge recontextualization back to home market.

Leveraging external resources for offensive content detection in social media

Hate speech is a burning issue of today’s society that cuts across numerous strategic areas, including human rights protection, refugee protection, and the fight against racism and discrimination. The gravity of the subject is further demonstrated by António Guterres, the United Nations Secretary-General, calling it “a menace to democratic values, social stability, and peace”. One central platform for the spread of hate speech is the Internet and social media in particular. Thus, automatic detection of hateful and offensive content on these platforms is a crucial challenge that would strongly contribute to an equal and sustainable society when overcome. One significant difficulty in meeting this challenge is collecting sufficient labeled data. In our work, we examine how various resources can be leveraged to circumvent this difficulty. We carry out extensive experiments to exploit various data sources using different machine learning models, including state-of-the-art transformers. We have found that using our proposed methods, one can attain state-of-the-art performance detecting hate speech on Twitter (outperforming the winner of both the HASOC 2019 and HASOC 2020 competitions). It is observed that in general, adding more data improves the performance or does not decrease it. Even when using good language models and knowledge transfer mechanisms, the best results were attained using data from one or two additional data sets.

Knowledge Transfer Mechanisms: The Case of the Mozambique Agrarian Research Institute from 2013 to 2015

Knowledge Transfer Mechanisms: The Case of the Mozambique Agrarian Research Institute from 2013 to 2015

Global human mobility and knowledge transfer: Highly skilled return migrants as agents of transnational learning

AbstractGlobal human mobility constitutes a key mechanism for knowledge transfer. This study examines the micro‐dynamics of knowledge transfer in the developed‐developing country migratory context. It highlights the agentic role of return migrants in transforming overseas learning into relevant knowledge in their home contexts. Drawing on situated and relational theories of knowledge and learning, the study views knowledge transfer as a relevance discovery process. It looks at a group of highly skilled migrants who had returned from developed countries to Ethiopia. Despite their high skill, the focus of knowledge transfer was mostly in non‐technical fields that include a broad range of organizational knowledge and work practices. These were made relevant to the local context through the returnees’ ‘work of reconciliation’, involving ‘engagement’, ‘alignment’ and ‘imagination’. The study challenges the standard assumption of a one‐way linear flow of knowledge from developed to developing countries. It sheds new light on the migration‐development link by highlighting the ‘aspirational’ aspect of migrant transnational learning.

Gendered asymmetry of access to knowledge for brucellosis control among pastoral communities in north-west Côte d’Ivoire

Brucellosis is an infectious zoonotic disease considered as a threat to public health and pastoralist livelihoods. Symptoms of the disease can lead to gender-specific ailments such as abortions in women and orchitis in men. Pastoralists and their families are at high risk of contracting the disease. Access to health information reinforces existing knowledge and contributes to disease prevention. However, in developing countries, interventions for knowledge sharing on zoonotic diseases predominantly target men. This study aimed to describe mechanisms of knowledge production and transfer on brucellosis according to gender, by assessing the way knowledge affects behaviours of pastoral communities. A community-based cross-sectional survey was conducted among a pastoral community (PC) of the Folon region in north-west Côte d’Ivoire. The study included transhumant pastoralists, sedentary livestock owners, shepherds and their wives. By using mixed methods, 26 semi-structured interviews were conducted, and 320 questionnaires were completed. Statistical analysis with chi-square (χ2) comparison tests was performed to compare variables between men and women. Findings were interpreted through the concept of specialisation of the social exclusion theory. We found that gender influences access to information on brucellosis and transfer of knowledge on brucellosis appeared gender-biased, especially from veterinarians towards men in the community. The social labour division and interventions of veterinarians through awareness reinforce the knowledge gap on brucellosis between men and women. Men and women consume raw milk, whilst only men in general handle animal discharges with bare hands. To improve the control of brucellosis, knowledge on best practice should be shared with pastoral communities using the One Health approach that encourages mutual learning. Innovative strategies based on gender daily tasks such as safe dairy processing by women and safe animal husbandry to expand their herd for men can be the entry point for the prevention of brucellosis.

Ethnozoological Study of Traditional Medicinal Animal Parts and Products Used among Indigenous People of Assosa District, Benishangul-Gumuz, Western Ethiopia

Traditional medicine is a global practice and depends on locally available natural resources and indigenous knowledge. Animals and their products have been used in the preparation of traditional remedies in various cultures since time immemorial. This study aimed to identify and document traditional medicinal sources from animals and associated indigenous knowledge in Assosa Districts, Benshangul Gumuz region, western Ethiopia, from September 2019 to July 2020 to ensure sustainable utilization of natural resources and biodiversity. A cross-sectional survey design was used to collect ethnozoological information with regard to animals used for medicinal purposes, parts used, ingredients added, ailments treated, method of preparation, mode of administration, dosage and duration of treatment, and the mechanisms of knowledge transfer. A total of 38 animal species were used as medicine to treat over 35 different kinds of human diseases including anatomical, physiological, psychological, and spiritual ailments and veterinary treatment. Over 15 animal species were found to score fidelity level (FL) more than 50%, of which the skin of Crocuta crocuta used for the treatment of evil eye had the highest FL (n = 35, 92.1%), followed by the blood of Sus scrofa domestica for treatment of stomach illness (n = 32, FL∼84.2%), the milk of Equus asinus to treat cough and eye disease (n = 28, Fl∼73.7%). The study area harbors diverse medicinal animals that represent key medical alternatives for local communities. The documentation of this indigenous knowledge of animal-derived medicine and the practice helps in developing strategies for conservations of biological diversities.

Evolutionary Multitasking for Feature Selection in High-Dimensional Classification via Particle Swarm Optimization

Feature selection (FS) is an important preprocessing technique for improving the quality of feature sets in many practical applications. Particle swarm optimization (PSO) has been widely used for FS due to being efficient and easy to implement. However, when dealing with high-dimensional data, most of the existing PSO-based FS approaches face the problems of falling into local optima and high-computational cost. Evolutionary multitasking is an effective paradigm to enhance global search capability and accelerate convergence by knowledge transfer among related tasks. Inspired by evolutionary multitasking, this article proposes a multitasking PSO approach for high-dimensional FS. The approach converts a high-dimensional FS task into several related low-dimensional FS tasks, then finds an optimal feature subset by knowledge transfer between these low-dimensional FS tasks. Specifically, a novel task generation strategy based on the importance of features is developed, which can generate highly related tasks from a dataset adaptively. In addition, a new knowledge transfer mechanism is presented, which can effectively implement positive knowledge transfer among related tasks. The results demonstrate that the proposed method can evolve a feature subset with higher classification accuracy in a shorter time than other state-of-the-art FS methods on high-dimensional classification.

Toward Large-Scale Evolutionary Multitasking: A GPU-Based Paradigm

Evolutionary multitasking (EMT), which shares knowledge across multiple tasks while the optimization progresses online, has demonstrated superior performance in terms of both optimization quality and convergence speed over its single-task counterpart in solving complex optimization problems. However, most of the existing EMT algorithms only consider handling two tasks simultaneously. As the computational cost incurred in the evolutionary search and knowledge transfer increased rapidly with the number of optimization tasks, these EMT algorithms cannot meet today’s requirements of optimization service on the cloud for many real-world applications, where hundreds or thousands of optimization requests (labeled as large-scale EMT) are often received simultaneously and require to be optimized in a short time. Recently, graphics processing unit (GPU) computing has attracted extensive attention to accelerate the applications possessing large-scale data volume that are traditionally handled by the central processing unit (CPU). Taking this cue, toward large-scale EMT, in this article, we propose a new EMT paradigm based on the island model with the compute unified device architecture (CUDA), which is able to handle a large number of continuous optimization tasks efficiently and effectively. Moreover, under the proposed paradigm, we develop the GPU-based <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">implicit</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">explicit</i> knowledge transfer mechanisms for EMT. To evaluate the performance of the proposed paradigm, comprehensive empirical studies have been conducted against its CPU-based counterpart in large-scale EMT.

Dynamic multitask optimization with improved knowledge transfer mechanism

Dynamic multitask optimization with improved knowledge transfer mechanism

Transfer Learning: A Paradigm for Machine Assisted Knowledge Transfer

This paper surveys transfer learning as a sustainable knowledge transfer mechanism. Conventional machine learning algorithms require a huge amount of labeled data for supervised learning. In absence of such data, the models suffer from performance degradation. Transfer learning enables the prior knowledge gained in doing a particular task to be reused or transferred to another new task of similar nature. This can speed up and improve the learning curve of the tasks in the new domain. The paper gives an overview of the transfer learning process and highlights how this innovative artificial intelligence (AI) technique can help achieve the goals of sustainable development. The literature survey highlights widely used mechanisms of Transfer Learning like homogeneous, heterogeneous, as well as instance-based, parameter-based, and relational-based implementation of transfer learning. It discusses how these mechanisms are utilized to create efficient AI-based applications which aid sustainability in the long run.

Evolutionary Many-Task Optimization Based on Multisource Knowledge Transfer

Multitask optimization aims to solve two or more optimization tasks simultaneously by leveraging intertask knowledge transfer. However, as the number of tasks increases to the extent of many-task optimization, the knowledge transfer between tasks encounters more uncertainty and challenges, thereby resulting in degradation of optimization performance. To give full play to the many-task optimization framework and minimize the potential negative transfer, this article proposes an evolutionary many-task optimization algorithm based on a multisource knowledge transfer mechanism, namely, EMaTO-MKT. Particularly, in each iteration, EMaTO-MKT determines the probability of using knowledge transfer adaptively according to the evolution experience, and balances the self-evolution within each task and the knowledge transfer among tasks. To perform knowledge transfer, EMaTO-MKT selects multiple highly similar tasks in terms of maximum mean discrepancy as the learning sources for each task. Afterward, a knowledge transfer strategy based on local distribution estimation is applied to enable the learning from multiple sources. Compared with the other state-of-the-art evolutionary many-task algorithms on benchmark test suites, EMaTO-MKT shows competitiveness in solving many-task optimization problems.

Interpersonal knowledge transfer within the multinational enterprise: incorporating identity based quasi-formal knowledge governance mechanisms

PurposeThe paper aims to incorporate the social identity theory perspectives to the knowledge-based view in order to suggest how certain organizational characteristics can be leveraged as knowledge governance mechanisms for interpersonal knowledge transfer within the multinational enterprise (MNE).Design/methodology/approachThis paper is a conceptual discussion on interpersonal knowledge governance mechanisms.FindingsThe paper proposes a new set of governance mechanisms which may be leveraged to govern interpersonal knowledge transfer. These mechanisms utilize organizational identity of individuals to govern individual level knowledge transfer behavior with the MNE. The paper also illustrates how subsidiary power, one of such mechanisms, influences interpersonal knowledge transfer within the MNE through organizational identification.Research limitations/implicationsAs the paper is conceptual, the proposed mechanisms have not been substantiated empirically. It calls for empirically testing the suggested mechanisms across countries.Practical implicationsThe paper provides insights to managers for leveraging on organizational identity to manage interpersonal level knowledge transfer within the MNE.Originality/valueThe paper adds organizational identity-based knowledge governance mechanisms to the knowledge governance approach. It highlights how certain organizational characteristics (e.g. subsidiary power), even though these are not knowledge governance mechanisms per se, can be utilized to govern interpersonal knowledge transfer with the MNE.

The ERP post-implementation stage: a knowledge transfer challenge

This paper examines the knowledge transfer process in ERP post-implementation projects, and specifically between the ERP project teams and the IT support team. Case studies were conducted in three large organizations and data was collected via semi-structured interviews. Descriptive and graphical representations were used to analyze knowledge transfer processes for each case and a cross-case analysis was performed. Results from this exploratory study shed light on the relation between the ERP evolution structure and the use of knowledge transfer mechanisms based on different types of knowledge (functional and technical). This paper highlights the necessity of relying on both formal and informal knowledge transfer mechanisms to cover recurring and ad hoc exchanges between the different stakeholders responsible for the evolution of an ERP. The paper also highlights the impact of the ERP integrator and its different inclusion strategies that are critical for the knowledge being shared by the ERP project stakeholders.

To Stem the Tide: Organizational Climate and the Locus of Knowledge Transfer

Prior work has maintained that organizations benefit from managing the transfer of proprietary knowledge. Transfer is often advantageous within organizational boundaries but may be harmful across them, because it might erode competitive advantage. Hence, we ask: How can organizations affect the direction in which knowledge flows? We examine the role of organizational climate as a governing mechanism for knowledge transfer. Our empirical strategy consists of a mixed-methods approach leveraging qualitative and experimental data over two cycles of theory building and theory testing. We start with an extensive field study of the European Organization for Nuclear Research (CERN), leveraging the insights from desk research, field observations, 53 interviews, and a laboratory-in-the-field experiment involving 518 physicists. We then provide a causal test of the emerging framework by means of two laboratory experiments with 389 participants. Our findings suggest employees are more likely to transfer knowledge to their colleagues when they identify as an integral part of the organization, but they would rather transfer knowledge to outside competitors when their organization encourages them to outperform coworkers. In the presence of an organizational climate that is unfavorable to preventing knowledge spillovers, we argue, organizations can redirect the locus of knowledge transfer internally by acting upon an individual employee’s job design and socialization regime. History: This paper has been accepted for the Organization Science Special Issue on Experiments in Organizational Theory. Funding: The authors acknowledge generous financial support from Bocconi University, the HEC Foundation of HEC Paris, and IESEG School of Management. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2021.1551 .