Search results

This study aims to highlight the inconsistency between conventional knowledge push judgements and the price of knowledge push. Also, a three-way decision-based relevant knowledge push algorithm was proposed.

Using a ratio of 80–20%, the experiment randomly splits the data into a training set and a test set. Each video is used as a knowledge unit (structure) in the research, and the category is used as a knowledge attribute. The limit is then determined using the user’s overall rating. To calculate the pertinent information obtained through experiments, the fusion coefficient is needed. The impact of the push model is then examined in comparison to the conventional push model. In the experiment, relevant knowledge is compared using three push models, two push models based on conventional International classification functioning (ICF), and three push models based on traditional ICF. The average push cost accuracy rate, recall rate and coverage rate are metrics used to assess the push effect.

The three-way knowledge push models perform better on average than the other push models in this research in terms of push cost, accuracy rate and recall rate. However, the three-way knowledge push models suggested in this study have a lower coverage rate than the two-way push model. So three-way knowledge push models condense the knowledge push and forfeit a particular coverage rate. As a result, improving knowledge results in higher accuracy rates and lower push costs.

This research has practical ramifications for the quick expansion of knowledge and its hegemonic status in value creation as the main methodology for knowledge services.

To the best of the authors’ knowledge, this is the first theory developed on the three-way decision-making process of knowledge push services to increase organizational effectiveness and efficiency.

This paper aims to amplify the retrieval and utilization of historical newspapers through the application of semantic organization, all from the vantage point of a fine-grained knowledge element perspective. This endeavor seeks to unlock the latent value embedded within newspaper contents while simultaneously furnishing invaluable guidance within methodological paradigms for research in the humanities domain.

According to the semantic organization process and knowledge element concept, this study proposes a holistic framework, including four pivotal stages: knowledge element description, extraction, association and application. Initially, a semantic description model dedicated to knowledge elements is devised. Subsequently, harnessing the advanced deep learning techniques, the study delves into the realm of entity recognition and relationship extraction. These techniques are instrumental in identifying entities within the historical newspaper contents and capturing the interdependencies that exist among them. Finally, an online platform based on Flask is developed to enable the recognition of entities and relationships within historical newspapers.

This article utilized the Shengjing Times·Changchun Compilation as the datasets for describing, extracting, associating and applying newspapers contents. Regarding knowledge element extraction, the BERT + BS consistently outperforms Bi-LSTM, CRF++ and even BERT in terms of Recall and F1 scores, making it a favorable choice for entity recognition in this context. Particularly noteworthy is the Bi-LSTM-Pro model, which stands out with the highest scores across all metrics, notably achieving an exceptional F1 score in knowledge element relationship recognition.

Historical newspapers transcend their status as mere artifacts, evolving into invaluable reservoirs safeguarding the societal and historical memory. Through semantic organization from a fine-grained knowledge element perspective, it can facilitate semantic retrieval, semantic association, information visualization and knowledge discovery services for historical newspapers. In practice, it can empower researchers to unearth profound insights within the historical and cultural context, broadening the landscape of digital humanities research and practical applications.

Previous research has analyzed the consequence of network stability; however, little is known about how partner type diversity influence network stability in R&D network. Based on knowledge-based view and social network theory, the purpose of this paper is to unravel the internal mechanisms between partner type diversity and network stability through the mediating role of knowledge recombination in R&D network.

The authors collected an unbalanced panel patent data set from information communication technology industry for the period 1994–2016. Then, the authors tested the different dimensions of partner type variety and its relevance in the R&D network and the mediating role of knowledge recombination through adopting the multiple linear regression.

Results indicate an inverted U-shaped relationship between partner type diversity (variety and relevance) and network stability, whereas knowledge recombination partially mediate these relationships.

From the perspective of R&D networks, this paper explores that there are the under-researched phenomena the antecedent of network stability through nodal attributes (i.e. partner type variety and partner type relevance). Moreover, this paper empirically examined the mediating role of knowledge recombination in the partner type diversity–network stability relationships. The novel perspective allows focal firm to recognize importance of nodal attributes, which are critical to fully excavate the potential capabilities of cooperating partners in R&D network.

This study examines how the characteristics of innovation knowledge exchanged among affiliate firms affect the ownership strategies adopted for their foreign subsidiaries.

This study employs a cross-classified multilevel model to examine a sample of 185 Korean manufacturing affiliates derived from 49 Chaebols engaged in international diversification, along with their 1,110 foreign manufacturing subsidiaries.

While exploratory innovation knowledge exchange lowers the affiliate's level of ownership in its foreign subsidiary, exploitative innovation knowledge exchange rather increases the affiliate's level of ownership in its foreign subsidiary.

This study advances the literature on intrafirm knowledge exchange by highlighting it as a determinant of ownership strategies. The study further shows that the characteristics of knowledge exchanged at the affiliate level not only determine the ownership structure but also have the potential to shape the direction in which the subsidiary develops its competencies.

This study has practical implications for the managers of business group affiliates. The results suggest that managers should adapt their ownership strategies according to the type of knowledge exchanged at the affiliate level to achieve a balanced and synergistic effect on intraorganizational knowledge exchange.

Previous studies have extensively explored the performance implications related to knowledge exchange. However, there is a notable gap in understanding the mechanisms through which the value of knowledge transferred within an affiliate is realized. To address this gap, this study focuses on ownership strategy as a crucial factor and empirically examines how the characteristics of innovation knowledge exchanged among affiliate firms influence the ownership strategies adopted for their foreign subsidiaries. By investigating this relationship, this study provides valuable insights into the complex dynamics of knowledge exchange and its effect on ownership decisions within business group affiliates.

This study aims to investigate the factors that lead to the failure of some rewards to induce knowledge sharing behavior among employees, with much focus on employees’ attitudes and leadership’s knowledge of employees’ preferences, and presents a model that depicts the linkages.

To investigate why the provision of some rewards fails to induce knowledge sharing behavior among employees, this study uses the preferred reporting items for systematic reviews and meta-analyses framework to identify and analyze 56 articles published from 2000 to 2021.

Knowledge sharing is positively linked to organizational performance. Further, employees’ negative attitudes toward a reward system negatively relate to knowledge sharing behavior. Furthermore, management’s lack of knowledge of employees’ preferences regarding rewards leads to the provision of incorrect rewards that do not enhance knowledge sharing behavior. Finally, a conceptual model depicting the linkages among the variables under consideration has been presented.

Through the present study, employees’ attitudes toward rewards and leadership’s knowledge of employees’ preferences have been presented as critical factors that can lead to the failure of some rewards to induce knowledge sharing behavior. Further, the conceptual framework that can guide managers and leaders in strategizing on how best to develop and implement correct reward systems has been presented.

The present study is a significant contribution to the literature by focusing on the negative side of rewards toward knowledge sharing behavior with a focus on employees’ attitudes and leadership’s awareness of employees’ preferences regarding rewards.

This paper aims to inquire into the awareness of Vietnamese architects about design for safety (DfS) and the level of engagement in applying DfS among them to get a generic view of the implementation of DfS in Vietnam.

Quantitative research was used, in which a questionnaire was sent to Vietnamese architects to evaluate how they consider and apply DfS in the design process. Inferential and descriptive statistics then analysed the obtained data to identify the role of each factor.

The results from the survey conclude that Vietnamese architects have low engagement in applying DfS despite their high awareness and positive attitude towards DfS. Besides, the participants showed the need for further DfS education and training, which is lacking in Vietnamese formal education. In addition, the research also confirms that DfS education and training have positive impacts on the frequency of DfS implementation in Vietnam.

This research contributes to the knowledge of DfS implementation in developing countries. In line with this, further studies on the DfS concept in developing countries are needed to draw a more objective overview and give the solution for the low DfS appliance.

To the best of the authors’ knowledge, this is the first study inquiring into the implication of DfS in Vietnam, contributing to improving the lack of knowledge in this field in developing countries and Vietnam in particular.

A novel facet of the construction industry's (CI) digital transformation relates to the rise of smart contracts, and the contribution of blockchain technology in this domain appears to be nascent but rapidly gaining traction. Although the benefits of digitalisation for technologically less enthusiastic CI are irrefutable, the adoption of smart contracts has been found to be low pertaining to industry professionals' behavioural factors stimulated by technological perception. The challenge undertook by this study, therefore, is to develop a knowledge framework for blockchain-enabled smart contract adoption in the CI.

From a methodological perspective, this study employed a qualitative approach that involved semi-structured interviews with ten (10) highly experienced CI practitioners involved in digital innovations for data collection. Directed content analysis was performed using NVivo 12 software, which enabled the creation of preliminary open codes. Subsequently, these open codes were grouped into similar categories to develop axial codes. Finally, the study presented final themes along with their corresponding descriptions.

Notably, research findings expanded the current body of knowledge on perceived attributes and their measurement items to determine the perception of innovation adoption in CI, where a total of nine (9) perceived attributes were associated with thirty-two (32) measurement items.

The measurement items were seen as having an extensive impact on the CI professionals' decision to adopt blockchain-enabled smart contracts. With ensuing implications, this study represents one of the first to present a knowledge framework exclusively customised for blockchain-enabled smart contracts, laying the groundwork for effective technological adoption by CI professionals.

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.

Construction professionals are the major contributors to developing a sustainable construction industry, whereas architects, engineers and quantity surveyors are the key construction professionals who must play extraordinary roles in achieving better sustainable construction. Therefore, this study aims to identify the job attributes of key Sri Lankan construction professionals in addressing challenges associated with climate change.

The study adopted a mixed research approach. A literature review and preliminary semi-structured interviews were used to appraise the job roles of architects, engineers and quantity surveyors in addressing challenges associated with climate change. The data collected through the qualitative approach were used in an online questionnaire survey, and the findings were analysed using the relative index method.

The findings highlight that regardless of the knowledge of the professional category on green rating tools, carbon footprint, adaptation of renewable energies for the reduction of energy consumption, building information modelling-related applications and waste management concepts/practices are the foremost job attributes required for the key Sri Lankan construction professionals in addressing challenges associated with climate change.

The results from this study provide a handful of guidance to construction industry professionals, national and international professional institutions, non-governmental organisations and other relevant authorities to address climate change within the built environment by identifying ways for improving the relevant key job attributes of construction industry professionals.

To the best of the authors’ knowledge, this is the first study that explores the job attributes of key Sri Lankan construction professionals in addressing the challenges associated with climate change.

This study aims to analyse the way the internal resources and their attributes contribute to the competitive advantages in an intensive organisation in knowledge of the pharmaceutical industry in an emerging market.

This is a qualitative case study focused where the NVivo software was used for information analysis and thematic analysis.

The outcomes showed that from the VRIO framework (value, rarity, imitability and organisation), the plant and equipment and the technical knowledge of its workers are the resources that, due to their attributes, especially the rare, those that grant an advantage competitive position compared to other companies in its sector. Those findings highlight that the resource-based view (RBV) is a good approximation to explain the construction of competitive advantage (CA) and, in addition, the relevance of rare attribute in pharmaceutical companies was confirmed.

The study points out empirical evidence on the relevancy of RBV, from the VRIO framework and the competitive profile matrix (CPM) for the analysis of the management of organisations from the emerging market (economy) perspective. The study also provides competitive advantage analysis tools with which managers can identify strategic resources for their companies.

The VRIO framework and CPM were integrated in the study to analyse the role of internal resources and their attributes in achieving CAs. This integration is the first time that it has been carried out in companies in the context of an emerging market.