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The research on social media-based academic communication has made great progress with the development of the mobile Internet era, and while a large number of research results have emerged, clarifying the topology of the knowledge label network (KLN) in this field and showing the development of its knowledge labels and related concepts is one of the issues that must be faced. This study aims to discuss the aforementioned issue.

From a bibliometric perspective, 5,217 research papers in this field from CNKI from 2011 to 2021 are selected, and the title and abstract of each paper are subjected to subword processing and topic model analysis, and the extended labels are obtained by taking the merged set with the original keywords, so as to construct a conceptually expanded KLN. At the same time, appropriate time window slicing is performed to observe the temporal evolution of the network topology. Specifically, the basic network topological parameters and the complex modal structure are analyzed empirically to explore the evolution pattern and inner mechanism of the KLN in this domain. In addition, the ARIMA time series prediction model is used to further predict and compare the changing trend of network structure among different disciplines, so as to compare the differences among different disciplines.

The results show that the degree sequence distribution of the KLN is power-law distributed during the growth process, and it performs better in the mature stage of network development, and the network shows more stable scale-free characteristics. At the same time, the network has the characteristics of “short path and high clustering” throughout the time series, which is a typical small-world network. The KLN consists of a small number of hub nodes occupying the core position of the network, while a large number of label nodes are distributed at the periphery of the network and formed around these hub nodes, and its knowledge expansion pattern has a certain retrospective nature. More knowledge label nodes expand from the center to the periphery and have a gradual and stable trend. In addition, there are certain differences between different disciplines, and the research direction or topic of library and information science (LIS) is more refined and deeper than that of journalism and media and computer science. The LIS discipline has shown better development momentum in this field.

KLN is constructed by using extended labels and empirically analyzed by using network frontier conceptual motifs, which reflects the innovation of the study to a certain extent. In future research, the influence of larger-scale network motifs on the structural features and evolutionary mechanisms of KLNs will be further explored.

The purpose of this paper is to solve the problem of low efficiency on knowledge resources allocation in the strategic emerging industry (SEI), an incentive model of technology innovation based on knowledge ecological coupling is designed.

First, a principal–agent model of knowledge inputs and a knowledge ecological coupling model based on an improved Lotka–Volterra model are constructed. In addition, a numerical example about Chongqing Yongchuan industrial park, the emulation analysis and the associated discussions are conducted to analyze the equilibriums of principal–agent in different knowledge inputs. Further, the paper analyzes the evolutionary equilibrium in knowledge ecological coupling and reveals the dual adjustments of the node organization on knowledge inputs.

Thus, this paper shows that by establishing the relationships of knowledge ecological coupling based on “mutualism and commensalism,” node organization raises the level of knowledge inputs; an incentive mode of “knowledge ecological coupling relationship + technology innovation chain” is conductive to substantially improving the efficiency of knowledge resource allocation, and to stimulate the vitality of node organization for technology innovation in the strategic emerging industry (SEI).

This paper contributes to the extant researches in two ways. First, this paper reveals the dual adjustments of the node organizations in inputting knowledge, which broadens the vision and borders of the researches on traditional knowledge management. The methods of the traditional principal–agent model and the knowledge input/output profit model are also expanded. Second, this paper verifies that applying the mode of “knowledge ecological coupling relationship + technology innovation chain” in practice is conducive to enhancing the efficiency of the cross-organizational knowledge allocation in the strategic emerging industry (SEI).

In the era of mobile internet, the social Q&A community has built a large-scale and complex knowledge label network through its internal knowledge units, and the scale and structure of the network have changed over time. By analysing the structural characteristics and evolution rules of knowledge label networks, the main purpose of this study is to understand the internal mechanisms of the replacement of old and new knowledge and the expansion of knowledge element boundaries, so as to explore the realization path of knowledge management in the new era from the perspective of complex networks.

This paper uses distributed crawlers to capture 419,349 samples from the Zhihu platform. Each sample contains 33 characteristic dimensions, and the natural year is used as the sliding window to divide the whole. In this study, the global knowledge label network and 11 local knowledge label networks are first constructed. Then, the degree distribution analysis and central node exploration of the knowledge label network are carried out using the complex network method. Finally, the average shortest path and average clustering coefficient of the network are analysed by the time series method, and the ARIMA model is used to predict the evolution of the correlation coefficient.

The research results show that the dissimilation degree of the degree distribution of the knowledge label network has gradually decreased from 2011 to 2021, and the attention of users in the knowledge community has shown a trend of distraction and diversification over time. With the expansion of the scale of the knowledge label network and the transformation to an information network, the network sparsity is becoming more and more obvious, and the knowledge granularity of the Q&A community is being refined and diversified. The prediction of the correlation coefficient of the knowledge label network by the ARIMA model shows that the connection between the labels is lacking diversity and the opinion strengthening phenomenon tends to strengthen, which is more likely to form the “echo chamber effect”, resulting in mutual isolation and even opposition between different circles. The Q&A community is about to enter a mature stage, and the corresponding status of each label has been finalized. The future development trend of label networks will be reflected in the substitution between labels, and the specific structure will not change significantly.

The Q&A community model is the trend in Web 2.0 community development. This study proves the effectiveness of complex networks and time series prediction methods in knowledge label network mining in the Q&A community.

This paper aims to study the characteristics and evolution rules of tagging knowledge network for users with different activity levels in question-and-answer (Q&A) community represented by Zhihu.

A random sample of issue tag data generated by topics in the Zhihu network environment is selected. By defining user quality and selecting the top 20% and bottom 20% of users to focus on, i.e. top users and bot users, the authors apply time slicing for both types of data to construct label knowledge networks, use Q-Q diagrams and ARIMA models to analyze network indicators and introduce the theory and methods of network motif.

This study shows that when the power index of degree distribution is less than or equal to 3.1, the ARIMA model with rank index of label network has a higher fitting degree. With the development of the community, the correlation between tags in the tagging knowledge network is very weak.

It is not comprehensive and sufficient to classify users only according to their activity levels. And traditional statistical analysis is not applicable to large data sets. In the follow-up work, the authors will further explore the characteristics of the network at a larger scale and longer timescale and consider adding more node features, including some edge features. Then, users are statistically classified according to the attributes of nodes and edges to construct complex networks, and algorithms such as machine learning and deep learning are used to calculate large-scale data sets to deeply study the evolution of knowledge networks.

This paper uses the real data of the Zhihu community to divide users according to user activity and combines the theoretical methods of statistical testing, time series and network motifs to carry out the time series evolution of the knowledge network of the Q&A community. And these research methods provide other network problems with some new ideas. Research has found that user activity has a certain impact on the evolution of the tagging network. The tagging network followed by users with high activity level tends to be stable, and the tagging network followed by users with low activity level gradually fluctuates.

Research has found that user activity has a certain impact on the evolution of the tagging network. The tagging network followed by users with high activity level tends to be stable, and the tagging network followed by users with low activity level gradually fluctuates. For the community, understanding the formation mechanism of its network structure and key nodes in the network is conducive to improving the knowledge system of the content, finding user behavior preferences and improving user experience. Future research work will focus on identifying outbreak points from a large number of topics, predicting topical trends and conducting timely public opinion guidance and control.

In terms of data selection, the user quality is defined; the Zhihu tags are divided into two categories for time slicing; and network indicators and network motifs are compared and analyzed. In addition, statistical tests, time series analysis and network modality theory are used to analyze the tags.

The objective of this paper is to propose a practical and operable method to identify and fill organisational knowledge gaps during new product development.

From a microscopic view, this paper introduces the tree-shaped organisational knowledge structure to formalise the knowledge gaps and their internal hierarchical relationships. Based on the organisational knowledge structure, organisational knowledge gaps are identified through tree matching algorithm. The tree-edit-distance method is introduced to calculate the similarity between two organisational knowledge structures for filling knowledge gap.

The proposed tree-shaped organisational knowledge structure can represent organisations' knowledge and their hierarchy relationships in a structured format, which is useful for identifying and filling organisational knowledge gaps.

The proposed concept of organisational knowledge structure can quantify organisational knowledge. The approach is valuable for strategic decisions regarding new product development. The organisational knowledge gaps identified with this method can provide real-time and accurate guidance for the product development path. More importantly, this method can accelerate the organisational knowledge gap filling process and promote organisational innovation.

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.

This paper seeks to present a comprehensive overview of the supply chain as an autopoietic system. The new autopoietic approach suggests a transition from traditional cognitivist epistemology to the theory of learning as a creational matter, and this type of thinking can potentially shed light on the role of knowledge creation as a part of supply chain management.

The paper is structured as follows: the first section describes the theoretical background of the concept of knowledge management in the supply chain. After that, the paper examines the general systems theory and the role of an autopoietic system within it. Then the paper addresses autopoietic epistemology. In particular, the notions of knowledge, learning, and knowledge flows are described so that the focus is on the context of the supply chain and supply chain management at operational level.

The supplier's, customer's, and firm's own organization and parts of the organization have autonomy system memories, which ultimately formulate how the intended development ideas are in fact realized and how they are adopted by the organization. Supply chain managers should take into account the fact that the routines and norms of the node are part of the system that are not controlled from outside. Instead, the system can modify its objectives internally as part of its autonomous operation, which should be taken into consideration in the knowledge sharing process.

The description of a supply chain as an autopoietic knowledge system is a new way to examine knowledge sharing in a supply chain.

Knowledge input development and innovation implementation are new features of industrial technology innovation. The purpose of this study is to find the process of coordination and ecological spiral in the ambidextrous innovation of industrial technology.

To design the model of industrial technology ambidextrous innovation based on knowledge ecology spiral, an input-output model of knowledge for ambidextrous innovation and a spiral model of knowledge ecology were constructed based on an improved Lotka-Volterra model. Then, the equilibriums in different knowledge inputs and the spiral evolution of knowledge ecology were analyzed. Finally, the ambidextrous coordination mechanism of the core organization was revealed.

By coordinating the knowledge inputs and the knowledge ecology spiral, enterprises extend the R&D investments in the innovation chain, which will facilitate the knowledge inputs of the exploitative and exploratory innovation. Implementing the ambidextrous coordination in the technology innovation chain and the knowledge ecology chain has the advantage of promoting knowledge inputs, mobility and ecological spiral. Meanwhile, it can achieve the “multi-source, integration and coordination” development of industrial technology innovation.

The two-element innovative knowledge input coordination model and the knowledge ecological spiral model based on the improved Lotka-Volterra model are constructed, which extends the modeling way of the traditional knowledge input-output profit model. It is expected to reduce the amount of knowledge input of a single member and provide theoretical reference for improving the efficiency of knowledge input by constructing the inter-dependent regenerative and inter-generative knowledge interaction.

This study aims at analyzing the features of knowledge flow and the role-specific nodes in knowledge networks among individuals and business units of six organizations in different industries, and suggesting prescriptions to prevent the organizational knowledge sclerosis.

This research conducts multiple case studies on the organizational knowledge paths of six companies in the multiple industries through social network analysis (SNA) tool developed by the authors of this paper.

This study provides four major findings which shed a new light on how to comprehend the features of knowledge flow and the role-specific nodes in knowledge networks in organizations: the within-business unit knowledge flows are more dominant over the inter-business units knowledge flow; the downward knowledge flows are dominant over the horizontal and upward knowledge flows in the management levels; distributions of knowledge owners and providers are like L-shape and the gap between knowledge owing and providing expands as the management levels go up; and the top 20 percent people in an organization dominate over a large portion of the knowledge brokerage activities.

Cultural difference issue might arise because data collection was limited to Korean organizations. Therefore, the findings from this study needs to be cautiously interpreted considering the cultural difference/deeper understanding of the organizational knowledge paths through social network lens can make it possible for more context-specific KM strategies (e.g. suitable for a specific functional unit, management level, or industry type) to be identified and implemented.

Managers can have a solid grasp about knowledge flows and knowledge node roles in their organization through social network analysis in order to facilitate the knowledge transfer and eliminate the knowledge link lapse in organizations.

This study could be a stepping stone for further empirical research since it expanded the level of organizational knowledge network analysis from individual and team to inter-unit and inter-management level through the block modeling analysis of knowledge network.

The present paper aims to explore how to measure trust as a receptivity force in an intra-organisational knowledge-sharing network with the help of self-developed algorithms of modelling percolations.

In this paper, a completely new methodology is applied by using a sample study of an international company’s financial centre as an example. Computer software has been developed to simulate the network and calculate the percolation thresholds by combining its characteristics, thereby revealing what and to what extent connectivity and trust, respectively, influence knowledge sharing.

The application of computer modelling to build up a percolation network is useful for answering questions about the determinants of knowledge sharing. Arguably, the authors demonstrate how the applied new methodology is superior in addressing how to measure the critical values of trust, connectivity and interaction issues, as well as leading to better insights about how these can be managed. The present paper confirms that trust is an essential factor influencing knowledge sharing and that there is a reciprocal effect between social interaction and trust.

The model provides a useful tool for assessing features of the intra-organisational knowledge-sharing network and thus an important foundation for implementing actions in practice. The findings of this study imply that managers should consider the important role of task-related trust between actors and in general for knowledge sharing. With the help of percolation modelling, the degree of trust in an organisation can be computed, and this provides managers with an approach for managing trust.

The topic of “how can trust be measured” is very important and is becoming even more important now because the financial crisis and other issues are raising questions about trust and moral compass rather than financial data. A percolation-based approach to studying knowledge sharing has not been researched in depth before now, and this study attempts to fill that gap. Fundamentally, this multidisciplinary research adds value to the theoretical foundation of the percolation network and research methodology to be used in social sciences and gives an example of their potential practical implications.