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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.

The purpose of this paper is to examine the relationship between knowledge-enhancing HR practices, intellectual capital and sustainable competitive advantage in knowledge-based firms.

A quantitative approach was used to conduct the present study. The respondents were sampled from knowledge-based firms in Iran. Overall, 320 managers in 157 firms were selected using convenience sampling. A structural equation model was employed for testing the hypotheses.

The study confirmed that knowledge-enhancing human resource (HR) practices established a very strong connection with intellectual capital. The results further disclosed a positive relationship between intellectual capital and firms' competitive advantage. A mediated relationship between knowledge-enhancing HR practices and firms' competitive advantage through intellectual capital was also affirmed.

The study was conducted in knowledge-based firms in Iran, which limits the generalizability of the research findings. Therefore, future studies should be carried out with samples from other contexts. Moreover, as the study was cross-sectional, the causal relationships could not be inferred directly.

The paper underscored the importance of intellectual capital in improving knowledge-enhancing HR practices and firms' competitive advantage. It suggests to human resource managers to make the organizational arrangements to design knowledge-enhancing HR practices, thereby developing the intellectual capital that brings competitive advantage to knowledge-based firms.

The results of this study contribute to advance research on the intellectual capital literature by trying to explain how intellectual capital as a mediator variable can influence the relationship between knowledge-enhancing HR practices and sustainable competitive advantage.

The purpose of this paper is to investigate the mediating roles of tacit and explicit knowledge sharing (KS) in linking the relationship between knowledge-based HRM practices and innovation competence of firms. This study also explores the potential moderating role of market turbulence in fostering the influence of KS behaviors on two forms of innovation competence namely radical innovation and incremental innovation.

The paper applied the quantitative approach and structural equation modeling to examine the correlation among the latent constructs based on the survey data collected from 293 participants in 115 firms.

The empirical findings of this study support the mediating role of KS behaviors in the relationship between knowledge-based HRM practices and aspects of innovation competence. It highlights the important role of market turbulence in stimulating the influence of KS behaviors on innovation capabilities.

Future research should investigate the impact of knowledge-based HRM practices on innovation capability via the mediating effects of knowledge management processes to bring better understanding of the importance of knowledge resources in organizations.

The paper significantly contributes to increasing knowledge and insights into the antecedent role of knowledge-based HRM practices, the mediating role of KS behaviors as well as the moderating role of market turbulence in fostering radical and incremental innovation, thereby advancing the body of comprehension of knowledge-based resources and innovation theory.

Fish quarantine is a measure to prevent the entry and spread of quarantine fish pests and diseases abroad and from one area to another within Indonesia's territory. Based on these backgrounds, this study aims to identify the knowledge, knowledge management (KM) processes and knowledge management system (KMS) priority needs for quarantine fish and other fishery products measures (QMFFP) and then develop a classification model and web-based decision support system (DSS) for QMFFP decisions.

This research methodology uses combination approaches, namely, contingency factor analysis (CFA), the cross-industry standard process for data mining (CRISP-DM) and knowledge management system development life cycle (KMSDLC). The CFA for KM solution design is performed by identifying KM processes and KMS priorities. The CRISP-DM for decision classification model is done by using a decision tree algorithm. The KMSDLC is used to develop a web-based DSS.

The highest priority requirements of KM technology for QMFFP are data mining and DSS with predictive features. The main finding of this study is to show that web-based DSS (functions and outputs) can support and accelerate QMFFP decisions by regulations and field practice needs. The DSS was developed using the CTree algorithm model, which has six main attributes and eight rules.

This study proposes a novel comprehensive framework for developing DSS (combination of CFA, CRISP-DM and KMSDLC), a novel classification model resulting from comparing two decision tree algorithms and a novel web-based DSS for QMFFP.

In the landscape created by digital transformation, developing the ability to adapt and innovate by absorbing and generating new knowledge has become a strategic priority for organizations. The theory of dynamic capabilities, especially from a knowledge-based perspective, has proven particularly useful in studying the phenomena of transformation and change. Moving from this premise, this paper aims to map the state of research and to define guidelines for the actualization of dynamic capabilities theory in the digital transformation era.

A structured literature review of 75 papers, using descriptive, bibliographic and content analysis, was performed to analyze the evolution of dynamic capabilities in the context of digital transformation.

Studies concerning knowledge-based dynamic capabilities for digital transformation have been clustered into five main research areas: the micro-foundation of dynamic capabilities for digital transformation; dynamic capabilities for value creation in digital transformation; dynamic capabilities for digital transition in specific industries; dynamic capabilities for “data-driven organizations”; and dynamic capabilities for digital transformation in SMEs and family firms. A future research agenda for scholars in strategic management is presented.

A conceptual framework and a future research agenda are presented to highlight directions for this promising research field concerning the renewal of dynamic capabilities in the context of digital transformation.

The originality of the paper lies in the conceptual framework aiming to systematize current research on knowledge-based dynamic capabilities for digital transformation and to provide a new conceptualization of digital dynamic capabilities, clarifying how organizations create and share knowledge in the era of digitalization.

The ongoing paradigm shift in the energy sector holds paramount implications for the realization of the sustainable development goals, encompassing critical domains such as resource optimization, environmental stewardship and workforce opportunities. Concurrently, this transformative trajectory within the power sector possesses a dual-edged nature; it may ameliorate certain challenges while accentuating others. In light of the burgeoning research stream on open innovation, this study aims to examine the intricate dynamics of knowledge-based industry-university-research networking, with an overarching objective to elucidate and calibrate the equilibrium of ambidextrous innovation within power systems.

The authors scrutinize the role of different innovation organizations in three innovation models: ambidextrous, exploitative and exploratory, and use a multiobjective decision analysis method-entropy weight TOPSIS. The research was conducted within the sphere of the power industry, and the authors mined data from the widely used PatSnap database.

Results show that the breadth of knowledge search and the strength of an organization’s direct relationships are crucial for ambidextrous innovation, with research institutions having the highest impact. In contrast, for exploitative innovation, depth of knowledge search, the number of R&D patents and the number of innovative products are paramount, with universities playing the most significant role. For exploratory innovation, the depth of knowledge search and the quality of two-mode network relations are vital, with research institutions yielding the best effect. Regional analysis reveals Beijing as the primary hub for ambidextrous and exploratory innovation organizations, while Jiangsu leads for exploitative innovation.

The study offers valuable implications to cope with the dynamic state of ambidextrous innovation performance of the entire power system. In light of the findings, the dynamic state of ambidextrous innovation performance within the power system can be adeptly managed. By emphasizing a balance between exploratory and exploitative strategies, stakeholders are better positioned to respond to evolving challenges and opportunities. Thus, the study offers pivotal guidance to ensure sustained adaptability and growth in the power sector’s innovation landscape.

The primary originality is to extend and refine the theoretical understanding of ambidextrous innovation within power systems. By integrating several theoretical frameworks, including social network theory, knowledge-based theory and resource-based theory, the authors enrich the theoretical landscape of power system ambidextrous innovation. Also, this inclusive examination of two-mode network structures, including the interplay between knowledge and cooperation networks, unveils the intricate interdependencies between these networks and the ambidextrous innovation of power systems. This approach significantly widens the theoretical parameters of innovation network research.

This study aims to examine how the knowledge breadth and depth of a patent affect its likelihood of being pledged. It also seeks to explore whether these relationships change diversely in different technological environments.

A complementary log-log model with random effects was conducted to test the hypotheses using a unique data set consisting of 348,927 invention patents granted by the China National Intellectual Property Administration from 1985 to 2015 belonging to 74,996 firms.

The findings reveal that both knowledge breadth and depth of a patent positively affect its likelihood of being pledged. Furthermore, the knowledge breadth and depth entail different degrees of superiority in different technological environments.

This study focuses on the effect of an individual patent’s knowledge base on its likelihood of being selected as collateral. It does not consider the influence of the overall knowledge characteristics of the selected patent portfolio.

Managers need to pay attention to patents’ knowledge characteristics and the changes in technological environments to select the most suitable patents as collateral and thus improve the success rate of pledge financing.

This study explores the impact of multidimensional characteristics of knowledge base on patent pledge financing within a systematic theoretical framework and incorporates technological environments into this framework.

Generative AI and more specifically ChatGPT has brought a revolution in the lives  of people by providing them with required knowledge that it has learnt from an exponentially large knowledge base. In this viewpoint, we are initiating the debate and offer the first step towards Generative AI based knowledge management systems in organizations.

This study is a viewpoint and develops a conceptual foundation using existing literature on how ChatGPT can enhance the KM capability based on Nonaka’s SECI model. It further supports the concept by collecting data from a public sector univesity in Hong Kong to strenghten our argument of ChatGPT mediated knowledge management system.

We posit that all four processes, that is Socialization, Externalization, Combination and Internalization can significantly improve when integrated with ChatGPT. ChatGPT users are, in general, satisfied with the use of ChatGPT being capable of facilitating knowledge generation and flow in organizations.

The study provides a conceptual foundation to further the knowledge on how ChatGPT can be integrated within organizations to enhance the knowledge management capability of organizations. Further, it develops an understanding on how managers and executives can use ChatGPT for effective knowledge management through improving the four processes of Nonaka’s SECI model.

This is one of the earliest studies on the linkage of knowledge management with ChatGPT and lays a foundation for ChatGPT mediated knowledge management system in organizations.

The purpose of this study is to explore growth models based on “industry-based capabilities”, “resources-based capabilities” and “institution-based capabilities” in the context of the determinants of micro, small and medium enterprises’ (MSMEs) actors’ intention to grow.

This study involved 188 owners/managers of MSMEs. The analysis was conducted using partial least squares structural equation modelling. Moreover, the importance-performance map analysis package is used to complement the study findings.

This study uses the framework of the resource-based view (entrepreneurial knowledge), industrial-based view (industrial linkage) and institutional-based view (government support and access to finance) as proxies of the tripod-based view framework. The findings show that entrepreneurial knowledge (skills, competencies and functional), government support, access to finance and industrial linkage (vertical and horizontal) significantly encourage MSMEs’ owner/manager growth intention.

Firstly, this study suggests that MSMEs actors focus on developing entrepreneurial knowledge to boost the skills, competencies and functionalities needed to improve their business capabilities, directly affecting their growth intention. Secondly, this study indicates that the growth intention of MSME players, besides increasing internal capacity, must also be supported by the external environment, such as financial institutions, government and industrial linkage.

This study offers a tripod-based view as a framework for MSMEs’ actors’ intention to grow, where the constructs in the model used so far have not been explored comprehensively in the context of MSMEs. So, the built model brings more relevant factors to explain this topic from various perspectives.