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Traditional case-adaptation methods have poor accuracy, low efficiency and limited applicability, which cannot meet the needs of knowledge users. To address the shortcomings of the existing research in the industry, this paper proposes a case-adaptation optimization algorithm to support the effective application of tacit knowledge resources.

The attribute simplification algorithm based on the forward search strategy in the neighborhood decision information system is implemented to realize the vertical dimensionality reduction of the case base, and the fuzzy C-mean (FCM) clustering algorithm based on the simulated annealing genetic algorithm (SAGA) is implemented to compress the case base horizontally with multiple decision classes. Then, the subspace K-nearest neighbors (KNN) algorithm is used to induce the decision rules for the set of adapted cases to complete the optimization of the adaptation model.

The findings suggest the rapid enrichment of data, information and tacit knowledge in the field of practice has led to low efficiency and low utilization of knowledge dissemination, and this algorithm can effectively alleviate the problems of users falling into “knowledge disorientation” in the era of the knowledge economy.

This study provides a model with case knowledge that meets users’ needs, thereby effectively improving the application of the tacit knowledge in the explicit case base and the problem-solving efficiency of knowledge users.

The adaptation model can serve as a stable and efficient prediction model to make predictions for the effects of the many logistics and e-commerce enterprises' plans.

This study designs a multi-decision class case-adaptation optimization study based on forward attribute selection strategy-neighborhood rough sets (FASS-NRS) and simulated annealing genetic algorithm-fuzzy C-means (SAGA-FCM) for tacit knowledgeable exogenous cases. By effectively organizing and adjusting tacit knowledge resources, knowledge service organizations can maintain their competitive advantages. The algorithm models established in this study develop theoretical directions for a multi-decision class case-adaptation optimization study of tacit knowledge.

This study aims to deal with the case adaptation problem associated with continuous data by providing a non-zero base solution for knowledge users in solving a given situation.

Firstly, the neighbourhood transformation of the initial case base and the view similarity between the problem and the existing cases will be examined. Multiple cases with perspective similarity or above a predefined threshold will be used as the adaption cases. Secondly, on the decision rule set of the decision space, the deterministic decision model of the corresponding distance between the problem and the set of lower approximate objects under each choice class of the adaptation set is applied to extract the decision rule set of the case condition space. Finally, the solution elements of the problem will be reconstructed using the rule set and the values of the problem's conditional elements.

The findings suggest that the classic knowledge matching approach reveals the user with the most similar knowledge/cases but relatively low satisfaction. This also revealed a non-zero adaptation based on human–computer interaction, which has the difficulties of solid subjectivity and low adaptation efficiency.

In this study the multi-case inductive adaptation of the problem to be solved is carried out by analyzing and extracting the law of the effect of the centralized conditions on the decision-making of the adaptation. The adaption process is more rigorous with less subjective influence better reliability and higher application value. The approach described in this research can directly change the original data set which is more beneficial to enhancing problem-solving accuracy while broadening the application area of the adaptation mechanism.

The examination of the calculation cases confirms the innovation of this study in comparison to the traditional method of matching cases with tacit knowledge extrapolation.

The algorithm models established in this study develop theoretical directions for a multi-case induction adaptation study of tacit knowledge.

This study designs a multi-case induction adaptation scheme by combining NRS and CBR for implicitly knowledgeable exogenous cases. A game-theoretic combinatorial assignment method is applied to calculate the case view and the view similarity based on the threshold screening.

This paper aims to offer a practical lens grounded in the relatively unexplored industry setting of medical devices. The objective of this paper is to use two in-depth case studies to highlight the key findings of an exploratory knowledge assessment framework surrounding the areas of knowledge creation, acquisition, sharing and reuse.

An interpretivist paradigm was followed while using two case studies. The study was developed over a three-year period using 36 in-depth interviews, document analysis and observation.

At the case companies, these findings were concluded: Across groups, cross-functional sharing is siloed, which leads to a lack of knowledge sharing. Cultural issues, such as hoarding of knowledge, hinder knowledge management (KM) initiatives. Employees new to the organisation find it difficult to locate knowledge. Employees are dependent on their informal network. The implementation of several KM initiatives is hindered because staff do not have sufficient time. Knowledge reuse is not given attention when targets have to be met. Due to time issues and informal network dependence, there is a lack of formal systems use. There is a lack of ownership of knowledge. There are knowledge retention problems. The organisation does not know its employees’ skills.

The usual limitations of case-study research apply surrounding generalisability; however, the author has used best practice in the application of this study using the case-study literature.

By exploring at firm level some of the factors associated with individual knowledge acquisition and providing empirical evidence, the study contributes to richer understanding of what should be perceived by potential knowledge recipients to enhance their acquiring knowledge from others. The research shows that for increased competitiveness, knowledge should be shared among organisational members and highlights some of the pitfalls of using KM systems to achieve this.

The proposed framework offers a lens to organisations with which they could gauge their knowledge base and ask the how and why questions. This would improve awareness in the areas of knowledge acquisition, sharing, learning and reuse.

The purpose of this paper is to provide a quantitative multicriteria decision‐making approach to knowledge management in construction entrepreneurship education by means of an analytic knowledge network process (KANP).

The KANP approach in the study integrates a standard industrial classification with the analytic network process (ANP). For the construction entrepreneurship education, a decision‐making model named KANP.CEEM is built to apply the KANP method in the evaluation of teaching cases to facilitate the case method, which is widely adopted in entrepreneurship education at business schools.

The study finds that there are eight clusters and 178 nodes in the KANP.CEEM model, and experimental research on the evaluation of teaching cases discloses that the KANP method is effective in conducting knowledge management to the entrepreneurship education.

As an experimental research, this paper ignores the concordance between a selected standard classification and others, which perhaps limits the usefulness of KANP.CEEM model elsewhere.

As the KANP.CEEM model is built based on the standard classification codes and the embedded ANP, it is thus expected that the model has a wide potential in evaluating knowledge‐based teaching materials for any education purpose with a background from the construction industry, and can be used by both faculty and students.

This paper fulfils a knowledge management need and offers a practical tool for an academic starting out on the development of knowledge‐based teaching cases and other teaching materials or for a student going through the case studies and other learning materials.

Knowledge retention is becoming a main challenge in many countries, as knowledge becomes a main asset of organizations. The research questions the challenge of how can organizations minimize the loss of important knowledge while experiencing high levels of retiree? The research aims to suggest a framework for knowledge retention initiatives in organizations.

The research methodology is multi‐case research. The unit of analysis is organization (eight organizations analyzed, overall more than 30 retiree knowledge retention mini projects). Data linkage to the propositions and method of interpretation – explanation building technique.

This research suggests that successful knowledge retention can be achieved in three primary stages: defining scope; documenting (planning and implementation); and integrating knowledge back into the organization. Special care must be dedicated throughout the process to: retaining best practices and unexpected situations; structuring the process of knowledge retention; structuring retained documentation.

Academic implications are two‐fold. First, it suggests that assessment projects, which estimate knowledge loss risk, and described in most academic researches, should be eliminated in knowledge retention models; second, research should continue, developing more models regarding detailed planning and implementation stages, as initiated in Hofer‐Alfeis DeLong and here. Further research should be conducted in order to discover how effective the suggested methods are in retrospect (after years, and not only after months).

Business implications do exist. The case studies described, using the proposed framework, show that knowledge retention is not only important, but also applicable. Structuring the process and results, as described above, may provide organizations with guidelines how to conduct such projects.

Its value is in changing the suggested known frameworks for knowledge retention, enabling more effective and efficient knowledge retention, and therefore less knowledge loss in organizations.

The purpose of this paper is to investigate the paradox that arises when firms simultaneously share and protect their knowledge in an alliance with other organizations. The goal of this paper therefore is to explore this tension field in such a coupled open innovation process and to identify which strategies can be developed to cope with this tension.

The study was initially guided by a literature review and exploratory interviews, and it ultimately develops an inductive framework based on a multiple case study approach. The paper presents eight cases of a focal firm involved in a particular R&D collaboration. The case studies are based on a variety of data sources, including a number of semi‐structured interviews.

This paper unravels the tension field of knowledge sharing and protection in R&D collaborations, with the knowledge characteristics at the core and with the knowledge embodiment and relational dimension as mediating factors. These forces are in turn influenced by the collaboration characteristics and environment. Moreover, the case studies show different ways to cope with the tension between knowledge sharing and protection, such as an open knowledge exchange strategy and a layered collaboration scheme with inner and outer members. Licensing is moreover presented as a concrete way to implement such coping strategies.

This paper provides an holistic perspective on the knowledge paradox in R&D collaborations as a coupled process of open innovation. Moreover, it describes two concrete strategies to cope with the tension field as well as the role and implications of licensing as a particular mechanism to overcome the open innovation paradox.

The purpose of this research is to provide useful insights for multinational corporations (MNCs) that intend to transfer knowledge to their subsidiaries situated in Mozambique. The local operating conditions particular to that country are influenced by three analytic dimensions: the source's ability to transfer knowledge, the climate of cooperation between source and recipient and the recipient's absorptive capacity.

The case study deals with four MNCs with subsidiaries in Mozambique which have developed activities in the industrial sector. The analysis is applied to those companies in order to answer the research questions.

The results obtained reveal that the recipient should not be made to feel that the transferred knowledge is imposed by the source, so that it can be used as an asset in the production of new products, but without reproducing the business model of the source. This process of adaptation to the specific local context is crucial.

The case study method does not permit the generalization of the results, but it does make it possible to study a combination of problems related to the phenomenon that contribute to a better understanding.

The purpose of this paper is to contribute to a better understanding of the process of knowledge transfer to subsidiary companies based in Mozambique, and greater efficiency in that process, taking into consideration the specific characteristics of the local market and the local absorptive capacity.

The next generation of Building Information Modelling (BIM) seeks to establish the concept of Building Knowledge Modelling (BKM). The current BIM applications in construction, including those for asset management, have been mainly used to ensure consistent information exchange among the stakeholders. However, BKM needs to utilise knowledge management (KM) techniques into building models to advance the use of these systems. The purpose of this paper is to develop an integrated system to capture, retrieve, and manage information/knowledge for one of the key operations of asset management: building maintenance (BM).

The proposed system consists of two modules; BIM module to capture relevant information and case-based reasoning (CBR) module to capture the operational knowledge of maintenance activities. The structure of the CBR module was based on analysis of a number of interviews and case studies conducted with professionals working in public BM departments. This paper discusses the development of the CBR module and its integration with the BIM module. The case retaining function of the developed system identifies the information/knowledge relevant to maintenance cases and pursues the related affected building elements by these cases.

The paper concludes that CBR as a tool for KM can improve the performance of BIM models.

As the research in BKM is still relatively immature, this research takes an advanced step by incorporating the intelligent functions of knowledge systems into BIM-based systems which helps the transformation from the conventional BIM to BKM.

The purpose of this paper is to show how Tai Chi (or T’ai Chi ch’uan) philosophy might be used in global project development. Collected case studies support a Tai Chi-based framework for global project teams to reduce stress and improve decision making through exercises, storytelling, and martial arts practices. The authors first proposed a model or procedural framework based on experiential knowledge from practicing Tai Chi while managing projects.

Analyzing case studies from knowledge managers, project managers, and executive leaders, the researchers collected data on applying the framework from a retrospective case study and from two observational case studies during project development. Tai Chi-based communications and exercises were shown to support critical thinking, knowledge sharing, and problem solving. The proposed framework and four-step procedure build on a global perspective to cultural awareness, creativity, and motivation as well as specific Tai Chi-based tactics, techniques, and operations for knowledge management. This preliminary study looks at improving collaboration in a competitive environment while supporting health, wellness, and work-life enjoyment.

Early research results suggest that teams and individuals working on projects and practicing Tai Chi might develop more cohesive strategies and improve soft skills during their integration of Eastern and Western philosophies.

Used case studies methodology, which provided examples of using Tai Chi during projects. Qualitative data used to develop the proposed framework. Also, interviews and discussion reviews conducted for additional validation collected on framework.

It is a pioneering, preliminary study. Future research with outcomes-based data from project managers using Tai Chi recommended.

The integration of Eastern and Western philosophies into a framework for team project and knowledge management was shown to support cohesive strategies, improve soft skills, and strengthen decision making.

A successful supply chain should ensure that all participating members benefit from the marketplace. To achieve this goal, the supply chain members need to improve their competences all the time, which requires a continuous learning process. Thus, mutual learning, through knowledge sharing between the different members, is a necessary approach to increase the competence of supply chain partners. To realise efficient and effective knowledge sharing in a supply chain, this paper aims to explore and formulate a model that supports an enterprise with its management of the supply chain members' knowledge resource sharing (herein referred to as “advanced practice” and includes two levels of knowledge – strategic and operational). The model is based on the theories of supply chain management (SCM) and case‐based reasoning (CBR).

This research follows a conductive and inductive cycle. Firstly, based on the learning expounded through an extensive literature survey regarding SCM and CBR, as well as available empirical applications, the conceptual model is designed. Then the primary stage evaluation will be discussed regarding the feasibility and refinement of the model towards its maturity.

To share knowledge along the supply chain is theoretically sound, but a difficult task to realise in practice, due to the complexity of knowledge sharing between the different organizations.

This research explores one of the important topics in SCM – knowledge sharing within a supply chain, and the model also extends and explores a new tool for this knowledge‐sharing process by applying CBR methodology.

The designed model in this research will provide a practice‐oriented vehicle allowing the supply chain members to share and apply their knowledge.

This research applies CBR in the domain of SCM, it both enriches the available approaches to supply chain performance enhancement and enlarges the application domains of CBR methodology.