Knowledge Transfer Capability Research Articles

Strategic internationalization decisions and dynamic capability deployment for the internationally growing firm (IGF)

Purpose This paper aims to explore the relationship between strategic internationalization decisions and dynamic capabilities deployment for the internationally growing firm (IGF). Dynamic capabilities refer to a firm’s ability to adapt proactively to a changing business environment, emphasizing the importance of “doing the right things” rather than just “doing things right. Design/methodology/approach Literature-based, this paper proposes a model that links internationalization decisions and dynamic capabilities deployment, offering valuable insights for both research and practical application. Findings The study highlights that the IGF – focused on expansion and growth abroad – faces unique complexities that demand “doing the right things” in terms of strategic internationalization decisions. Three critical organizational capabilities – knowledge transfer, knowledge recombination and learning capabilities – are mechanisms linking strategic internationalization decisions to dynamic capability deployment in the IGF. These organizational capabilities enable the IGF to act entrepreneurially and deploy dynamic capabilities across borders. Research limitations/implications The model provides a practical framework illustrating the interconnectedness of strategic internationalization decisions and their combined effects on the ability of IGF to deploy dynamic capabilities to adapt to a changing global environment. Originality/value This research addresses a gap in the literature, challenging the conventional assumption that dynamic capabilities precede firms’ decisions to internationalize and that these dynamic capabilities can only be enhanced abroad.

Incremental Data Drifting: Evaluation Metrics, Data Generation, and Approach Comparison

Incremental data drifting is a common problem when employing a machine-learning model in industrial applications. The underlying data distribution evolves gradually, e.g., users change their buying preferences on an E-commerce website over time. The problem needs to be addressed to obtain high performance. Right now, studies regarding incremental data drifting suffer from several issues. For one thing, there is a lack of clear-defined incremental drift datasets for examination. Existing efforts use either collected real datasets or synthetic datasets that show two obvious limitations. One is in particular when and of which type of drifts the distribution undergoes is unknown, and the other is that a simple synthesized dataset cannot reflect the complex representation we would normally face in the real world. For another, there lacks of a well-defined protocol to evaluate a learner’s knowledge transfer capability on an incremental drift dataset. To provide a holistic discussion on these issues, we create approaches to generate datasets with specific drift types, and define a novel protocol for evaluation. Besides, we investigate recent advances in the transfer learning field, including Domain Adaptation and Lifelong Learning, and examine how they perform in the presence of incremental data drifting. The results unfold the relationships among drift types, knowledge preservation, and learning approaches.

DGTRL: Deep graph transfer reinforcement learning method based on fusion of knowledge and data

Deep reinforcement learning has shown promising application effects in many fields. However, issues such as low sample efficiency and weak knowledge transfer and generalization capabilities constrain its in-depth development. This paper proposes a deep graph transfer reinforcement learning method based on knowledge and data fusion. Through knowledge transfer between tasks and data training in exploration, efficient learning driven by knowledge and data fusion is realized. First, we map the trajectories of agents in the source and target domains into graph structures to represent knowledge and environmental models and use a graph matching method to match the knowledge adaptation domain based on similarity. Second, real-time transfer of source domain knowledge is carried out in a similar mapping domain, and policy mapping, verification, and adaptive transfer mechanisms based on knowledge reuse frequency are designed to effectively improve the transfer effect. Finally, we evaluate and validate the proposed algorithm, mechanism, and their application effectiveness in diverse and multitask knowledge transfer scenarios using three indicators in environments such as maze. The results show that the method improves the target task time indicators by 84.7% and 70% under the conditions of no verification exploration and a standard transfer setting, respectively, achieving significant knowledge reuse effects.

Exploring the dynamic capabilities of technology provider ecosystems: A study of smart manufacturing projects

This study examines the capabilities of technology provider ecosystems within smart manufacturing implementation projects. Whilst the study of capabilities for technology implementation is well acknowledged, the existing literature lacks a focused analysis on the dynamic capabilities required from ecosystems of technology providers engaging with adopter firms for the development of smart manufacturing solutions. More specifically, research has overlooked how such provider capabilities address the adopter's requirements and facilitate the related innovation outcomes for the adopter firm. Thus, our findings provide several contributions to the literature. Firstly, by examining two smart manufacturing projects within Pharmaceutical and Semi-conductor manufacturing contexts, we provide an in-depth analysis of the complexity of adopter requirements. Secondly, we uncover the nature of three main technology provider dynamic ecosystem capabilities. These reflect comprehensive skills in technology search and learning, project implementation, and knowledge transfer capabilities supporting the development of solutions for decision-making and predictive maintenance. Thirdly, we reveal how provider ecosystems build on these capabilities to address the complex requirements of the adopter firm and facilitate different types of process innovation outcomes. Respectively linked to performance, sustainability and evolving process sustainability.

How does multidimensional interaction of knowledge transfer affects digital innovation capability?

ABSTRACT This study establishes the link between multi-dimensional interaction of knowledge transfer paths and digital innovation capability from both macro−global (native, cross−border) and micro−knowledge type (auxiliary and complementary) perspectives. Using the data of Chinese firms, we find that the matching of the “supply” of native knowledge network and auxiliary knowledge “demand” is more conducive to the improvement of digital innovation capability than matching of the “demand” of complementary knowledge. The matching of the “supply” of cross−border knowledge network and the “demand” of complementary knowledge are more conducive to the improvement of digital innovation capability than matching with the “demand” of auxiliary knowledge. Digital environment scanning capability and knowledge flow coupling moderates the relationship between complementary and auxiliary knowledge transfer and digital innovation capability. Our findings have important implications for the construction of digital innovation network systems.

Knowledge Transfer and Reuse: A Case Study of Ai-Enabled Resource Management in RAN Slicing

An efficient resource management scheme is critical to enable network slicing in 5G networks and in envisioned 6G networks, and artificial intelligence (AI) techniques offer promising solutions. Considering the rapidly emerging new machine learning techniques, such as graph learning, federated learning, and transfer learning, a timely survey is needed to provide an overview of resource management and network slicing techniques of AI-enabled wireless networks. This article provides such a survey along with an application of knowledge transfer in radio access network (RAN) slicing. In particular, we first provide some background on resource management and network slicing, and review relevant state-of-the-art AI and machine learning (ML) techniques and their applications. Then, we introduce our AI-enabled knowledge transfer and reuse-based resource management (AKRM) scheme, where we apply transfer learning to improve system performance. Compared with most existing works, which focus on the training of standalone agents from scratch, the main difference of AKRM lies in its knowledge transfer and reuse capability between different tasks. Our paper aims to be a roadmap for researchers to use knowledge transfer schemes in AI-enabled wireless networks, and we provide a case study over the resource allocation problem in RAN slicing.

Do green foreign direct investments increase the innovative capability of MNE subsidiaries?

Technologies to mitigate climate change may diffuse from lead markets to the rest of the world through several mechanisms and make important contributions to the global green transformation. In this paper, we explore the role played by multinational enterprises (MNEs) in transferring knowledge and innovative capabilities in green technologies to their global subsidiaries. We posit that the degree of green knowledge transfer and innovative capability development in subsidiaries depend on: (i) the host country characteristics, (ii) the specific technology in question, and (iii) the mode of entry. The empirical analysis combines data on foreign direct investments with patent analysis. The results suggest that being a subsidiary of a green MNE has a positive impact on the number and quality of green patents produced locally. This green innovative advantage vis-à-vis domestic companies is larger in less developed countries and in those that are less reliant on oil rents, in particular if they already possess higher levels of relevant domestic innovative capacity. Furthermore, firm and sectoral characteristics also matter. The analysis suggests that green FDIs are more effective when technologies are characterized by low tradability and tacit knowledge. Finally, cross-border acquisitions are more efficient at strengthening green innovative capabilities than subsidiaries established with greenfield investments.

See you somewhere in the ocean: few-shot domain adaptive underwater object detection

The current data-driven underwater object detection methods have significantly progressed. However, there are millions of marine creatures in the oceans, and collecting a corresponding database for each species for similar tasks (such as object detection)is expensive. Besides, marine environments are more complex than in-air cases. Water quality, illuminations, and seafloor topography may lead to domain shifting with visual instability features of underwater objects. To tackle these problems, we propose a few-shot adaptive object detection framework with a novel two-stage training approach and a lightweight feature correction module to accommodate both image-level and instance-level domain shifting on multiple datasets. Our method can be trained in a source domain and quickly adapt to an unfamiliar target domain with only a few labeled samples. Extensive experimental results have demonstrated the knowledge transfer capability of the proposed method in detecting two similar marine species. The code will be available at: https://github.com/roadhan/FSCW

TEsoNet: knowledge transfer in surgical phase recognition from laparoscopic sleeve gastrectomy to the laparoscopic part of Ivor–Lewis esophagectomy

BackgroundSurgical phase recognition using computer vision presents an essential requirement for artificial intelligence-assisted analysis of surgical workflow. Its performance is heavily dependent on large amounts of annotated video data, which remain a limited resource, especially concerning highly specialized procedures. Knowledge transfer from common to more complex procedures can promote data efficiency. Phase recognition models trained on large, readily available datasets may be extrapolated and transferred to smaller datasets of different procedures to improve generalizability. The conditions under which transfer learning is appropriate and feasible remain to be established.MethodsWe defined ten operative phases for the laparoscopic part of Ivor-Lewis Esophagectomy through expert consensus. A dataset of 40 videos was annotated accordingly. The knowledge transfer capability of an established model architecture for phase recognition (CNN + LSTM) was adapted to generate a “Transferal Esophagectomy Network” (TEsoNet) for co-training and transfer learning from laparoscopic Sleeve Gastrectomy to the laparoscopic part of Ivor-Lewis Esophagectomy, exploring different training set compositions and training weights.ResultsThe explored model architecture is capable of accurate phase detection in complex procedures, such as Esophagectomy, even with low quantities of training data. Knowledge transfer between two upper gastrointestinal procedures is feasible and achieves reasonable accuracy with respect to operative phases with high procedural overlap.ConclusionRobust phase recognition models can achieve reasonable yet phase-specific accuracy through transfer learning and co-training between two related procedures, even when exposed to small amounts of training data of the target procedure. Further exploration is required to determine appropriate data amounts, key characteristics of the training procedure and temporal annotation methods required for successful transferal phase recognition. Transfer learning across different procedures addressing small datasets may increase data efficiency. Finally, to enable the surgical application of AI for intraoperative risk mitigation, coverage of rare, specialized procedures needs to be explored.Graphical abstract

Knowledge transfer capability: born or built

Knowledge transfer capability: born or built

Knowledge transfer capability: born or built

Knowledge transfer capability: born or built

ROLE OF TOTAL QUALITY MANAGEMENT TOWARDS ORGANIZATIONAL PERFORMANCE THROUGH KNOWLEDGE TRANSFER AND INNOVATION CAPABILITIES

This research examines relationship between total quality management and organizational performance with the mediating effect of knowledge transfer and Innovation capabilities. This research encourages investigation of the several logical factors that characterize overall quality management. The researcher used quantitative survey research approach to conduct this research. The population of the study was consisted of 240 textile industry employees in Faisalabad, Pakistan. Results show that quality management significantly effect on organizational performance. The results indicated that knowledge transfer and Innovation Capabilities mediate the relationship between total quality management and organizational performance. This study provides foundation for specialists to customize quality management practices under various situational factors while involving the knowledge- sharing and innovation capabilities of the employees by recognizing two distinct crucial directions of QM practices. This paper mainly contributes to the study of organizational context enablers for total quality management, especially when firms are seeking to build/improve innovation capabilities to enhance organizational performance.

Impact of Total Quality Management on organizational performance with mediating effects of knowledge transfer and Innovation capabilities of Employees

This study analyzes the connection between quality management practices and organizational performance and simultaneously whether internal knowledge transfer and innovation capabilities of employees mediates this relationship or not. This study of Total Quality Management is embraced by analyzing different logical variables that form it. Hypotheses are created on the connection between some quantity management practices, inside knowledge transfer, innovation capabilities and organizational performance. Data was composed from quality managers of textile firms of Faisalabad. 280 Questionnaires were distributed among the people those who are well knowledgeable about the technicalities of TQM principles. The final data of 241 questionnaires received back has been considered for further analysis purpose. The outcomes affirm the significance of the QM context-oriented factors on organizational performance in the presence of internal knowledge sharing and innovation abilities of employees. More investigation into the efficacy of quality management could be brought about by adopting a more holistic perspective of the supply chain. This perspective should explore how manufacturers, suppliers, and customers collaborate with one another.

Learning From Peers: Deep Transfer Reinforcement Learning for Joint Radio and Cache Resource Allocation in 5G RAN Slicing

Network slicing is a critical technique for 5G communications that covers radio access network (RAN), edge, transport and core slicing. The evolving network architecture requires the orchestration of multiple network resources such as radio and cache resources. In recent years, machine learning (ML) techniques have been widely applied for network management. However, most existing works do not take advantage of the knowledge transfer capability in ML. In this paper, we propose a deep transfer reinforcement learning (DTRL) scheme for joint radio and cache resource allocation to serve 5G RAN slicing. We first define a hierarchical architecture for joint resource allocation. Then we propose two DTRL algorithms: Q-value-based deep transfer reinforcement learning (QDTRL) and action selection-based deep transfer reinforcement learning (ADTRL). In the proposed schemes, learner agents utilize expert agents’ knowledge to improve their performance on current tasks. The proposed algorithms are compared with both the model-free exploration bonus deep Q-learning (EB-DQN) and the model-based priority proportional fairness and time-to-live (PPF-TTL) algorithms. Compared with EB-DQN, our proposed DTRL-based method presents 21.4% lower delay for Ultra Reliable Low Latency Communications (URLLC) slice and 22.4% higher throughput for enhanced Mobile Broad Band (eMBB) slice, while achieving significantly faster convergence than EB-DQN. Moreover, 40.8% lower URLLC delay and 59.8% higher eMBB throughput are observed with respect to PPF-TTL.

MULTI-CRITERIA OPTIMIZATION FOR KNOWLEDGE EXTENSIONISTS IN INTERNAL KNOWLEDGE TRANSFER OF HIGHER EDUCATION IN NIGERIA

The demand for high education in an environment of an insufficient number of experts requires efficient knowledge management. The introduction of knowledge extensionists in higher learning institutions remains relevant to bridge the gap created by this imbalance. In this work, we developed a new multi-objective mathematical model for optimizing the knowledge transfer capability of extensionists and knowledge management in higher learning institutions in Nigeria. The Model provides the information needed for a detailed analysis of the design trade-off between conflicting objectives that are most interesting to a decision-maker. The binary Integer MultiObjective Model is a suitable model for a population of potential solutions. The Model focused on practical factors for effective knowledge transfer. Several indices were considered, such as number of task types, number of clusters (groups), number of employees, etc., as the knowledge transfer (KT) assessment factors. Some parameters are adequate to recognize the KT potential in an employee, such as whether task type can be handled by an employee, whether the employee is interested in cooperating with another employee, used the minimum size of the cluster in terms of the number of employees. The concept of “interest to work with each other” is a novel aspect considered in the recommended algorithm. Therefore, for illustration, an intelligent algorithm based on matrix and clustering concepts to evaluate data collected via a questionnaire from Waziri Umaru Federal Polytechnic Birnin Kebbi, Nigeria was developed. The model is suitable for any given set of data to be applied elsewhere

Trust as a Key Factor in Knowledge Transfer and Innovation Capabilities

The era of knowledge-based economy has created rapid changes in the business environment. Maintaining a business in that era requires innovation to maintaining its competitiveness. Innovation requires knowledge resources, where knowledge transfer plays an essential role in creating new knowledge that can be utilized to improve innovation capabilities. However, knowledge transfer is considered not a simple process because of the stickiness of knowledge, but on the other hand, knowledge transfer can be regarded as something that can happen instantly. Trust is therefore considered as the factor to strengthening the causality relationship between knowledge transfer and innovation capabilities. This study used a survey approach with Partially Least Square (PLS) data analysis techniques. The respondents in this research are SMEs of the creative industry handicraft sub-sector in the Province of DIY, Bali and North Sulawesi, which are justified as representations of Indonesia. Based on the 201 data collected, it was found that trust significantly moderated the causality relationship between knowledge transfer and innovation capabilities. Hence, knowledge transfers are needed for developing SMEs of the creative industries innovation capabilities which strengthened by the trust.

Knowledge Transfer Capabilities and Owning vs. Franchising Decision for Hospitality Chains

Knowledge Transfer Capabilities and Owning vs. Franchising Decision for Hospitality Chains

Knowledge Transfer Capabilities and Owning vs. Franchising Decision for Hospitality Chains

Knowledge Transfer Capabilities and Owning vs. Franchising Decision for Hospitality Chains

Deep Self-Supervised Domain Adaptation Network for Fault Diagnosis of Rotating Machine With Unlabeled Data

Recently, domain adaptation (DA)-based fault diagnosis (FD) approaches have been receiving increasing attention in intelligent FD of rotating machinery due to its powerful knowledge transfer capability on different working conditions. The existing methods traditionally assume that the number of fault categories between the source and target domains is equal. However, this assumption generally does not conform to the real industrial scenarios, where the coincidence degree between two domains is uncertain due to the unlabeled data obtained from the target domain. Therefore, a more general framework is studied to satisfy unclear DA scenarios. This demanding DA problem is solved in this article using deep self-supervised DA network. First, a novel neighborhood clustering technique is used to cluster fault samples in a self-supervised way. Second, an entropy-based feature alignment is introduced into the proposed method to separate the known and unknown samples in source and target domains. The experimental results on a gearbox dataset demonstrate that the proposed method is hopeful for actual industrial applications.

Depth Prototype Clustering Method Based on Unsupervised Field Alignment for Bearing Fault Identification of Mechanical Equipment

In the process of mechanical equipment fault diagnosis, it is difficult to obtain enough labeled samples due to the changeable operating conditions, complex working environment, and the limitation of measuring equipment. Therefore, a prototype clustering method based on unsupervised domain alignment is proposed for fault detection and diagnosis of mechanical equipment under unmarked samples. First, the relative maximum mean discrepancy index is constructed to map the data collected from different devices to the same high-dimensional space for feature alignment. Then, cluster the output and calculate the prototype for each category. Compared with the classification directly using the softmax function, the prototype clustering method can correct the local possible misjudged points through the overall average distribution. By minimizing the global distance error, prototypes obtained by clustering in the target domain are matched with prototypes of tagged samples in the source domain and, finally, realize unsupervised state recognition. Six experiments are designed to verify the effectiveness of the proposed method. The accuracy of the proposed method is 1%–2% higher even when compared to one of the most advanced fault diagnosis methods available. Also, it can be found from the results of t-stochastic neighbor embedding (SNE) visualization that the proposed method has higher knowledge transfer capability.