Virtual Knowledge Communities Research Articles

Factors Influencing Knowledge-Sharing Intention in Virtual Waste Management Community: A Systematic Review and AHP Analysis

Most people are unaware of the dangers of waste, so they do not reduce waste. Knowledge and awareness of a system are essential perspectives for realizing socio-economic desires. Virtual communities are a means to share knowledge about proper waste management. Based on previous literature searches, research has yet to identify the factors influencing knowledge-sharing intentions (KSI) in virtual communities focusing on waste management issues. This literature review examines previous research regarding the factors influencing KSI using the Kitchenham method in virtual communities focusing on waste management. This literature review also examines previous research in the context of the higher education community and professional organizations. This literature review resulted in the extraction of data from previous research, including the type of knowledge disseminated, methods of sharing knowledge, technology used to share knowledge, and factors that influence KSI. Thirty-nine factors influence KSI, mapped into five domains: organizational culture, information technology (IT) infrastructure, individuals, perceived risks, and perceived benefits. These factors were then weighted using the Analytical Hierarchy Process (AHP) method to obtain 15 factors based on the characteristics of the community that shared knowledge about waste management. Based on the AHP weighting, it is known that the IT domain infrastructure is the domain that has the highest weight. This research contributes to the academic field, namely the availability of a framework regarding the factors influencing the intention to share knowledge in virtual communities focusing on waste management in Indonesia. This research also provides a practical contribution, namely a framework that can be used by environmental practitioners to develop policies to support environmental sustainability initiatives.

The Stochastic Evolution Game of Knowledge-Sharing Incentive in Virtual Communities under Intelligent Recommendation Systems

To effectively solve the issue of unsmooth knowledge sharing in virtual communities under intelligent recommendation systems, we analyzed the impact of key factors on the strategy selection and evolution path in different scenarios.Based on the principles of bounded rationality and benefit maximization, we consider the principles of evolutionary game theory and the influence of random interference. A random evolutionary game model is constructed to analyze strategy selection in the process of knowledge sharing incentives between virtual community platforms and users. We obtained the evolutionary equilibrium strategies under different parameter restrictions and analyzed the evolutionary stability of the dynamic game process of knowledge sharing incentives.The research shows that interference from random factors affects both the speed and the trend of strategy evolution in virtual community platforms and users. In order to improve the enthusiasm of users to share, virtual community need to increase the proportion of users with positive feedback, improve the recommendation incentive mechanism, and reduce the user loss coefficient. Based on the above research conclusions, some countermeasures and suggestions for improving the performance of virtual community knowledge sharing under intelligent recommendation systems are proposed, which provides theoretical guidance for knowledge sharing among virtual community members.

From screen to society: second language learners’ cultural adaptation and identity reconstruction in virtual knowledge communities

PurposeThe purpose of this study is to examine the impact of computer-assisted language learning on cultural adaptation and language learning in non-traditional classroom settings.Design/methodology/approachThe data of this study came from extended periods of observation, multiple rounds of semi-structured interviews and home visits. Using narrative inquiry, it analyzes an immigrant's journey of language learning and cultural adaptation within a virtual knowledge community.FindingsThe findings of this study reveal the profound impact of virtual knowledge communities on enhancing second language learning and facilitating cultural adaptation.Originality/valueThis study offers original insights by demonstrating the transformative power of virtual knowledge communities for the purpose of second language acquisition and cultural adaptation.

Blockchain Tokens for Enterprise Knowledge Sharing

Virtual knowledge communities have become essential platforms for enterprises to facilitate knowledge sharing. However, due to the lack of effective incentive mechanisms, the traditional virtual knowledge community is generally inefficient in attracting high-quality content and maintaining enthusiasm for users' long-term participation. To address these problems, we theoretically explore the characteristics and economic ecology of enterprise knowledge sharing based on blockchain tokens to promote its sustained development. Specifically, we examine the application scenarios of token-economy activities through system-dynamics simulation and discuss the development status and path of the economic ecology. In addition, we studied the blockchain token-based incentive and penalty mechanisms for virtual knowledge communities to better stimulate knowledge sharing. Finally, the research method is applied to the GienTech supply chain finance community website to validate the proposed model and analysis. The findings contribute to the existing literature and point enterprises in a new direction in designing more effective incentive mechanisms to improve knowledge-sharing in virtual knowledge communities.

Platform Matters: How Can Virtual Knowledge Communities Achieve Value Co-Creation Among Users in the Sharing Economy?

Under the sharing economy model, how value is co-created has changed significantly, with users beginning to participate more actively and proactively in the value creation process of enterprises, gradually becoming the core driver of value creation. Virtual knowledge communities have developed rapidly among the application areas of the sharing economy. In the context of high-quality development, the flourishing development of virtual knowledge communities helps promote the entire flow of knowledge in society and better serves the national innovation-driven development strategy. At the same time, a series of problems have emerged in the rapid development of virtual knowledge communities, such as poor user experience, a single way of realization and uneven content in the communities. As an important channel and hub for users to exchange information and acquire knowledge, the core problem that virtual knowledge communities need to solve is attracting users and motivating them to share their expertise and actively participate in the value-creation process. Based on the Zhihu case, this paper aims to explore the value co-creation mechanism of users in virtual knowledge communities to optimize platform services, enhance user participation and promote the exchange and dissemination of knowledge.

A Dynamic Prediction Neural Network Model of Cross-Border e-Commerce Sales for Virtual Community Knowledge Sharing.

In this paper, a neural network algorithm is used to conduct in-depth research and analysis on the sales dynamics prediction of virtual community knowledge sharing in cross-border e-commerce. Both the expected returns and the social network structure are analyzed, and both have positive effects on knowledge sharing in the actual development process, but the degree of them also possesses certain variability. A model of the factors influencing the quality of knowledge shared by users is constructed to explore the relationship between the dimensions in social capital and how they affect the community users' perceptions of knowledge quality. Exploring the strong influencing factors of product repurchase rate has key implications for promoting product sales and sales forecasting. The scale of this paper has undergone several minor revisions, and the content validity is very good. Criterion validity is generally reflected by Person correlation coefficient, and construct validity includes exploratory factor analysis and confirmatory factor analysis. First, the values were clustered, and the optimal variables were selected using stepwise regression and fitted with Poisson regression models to explore the relationship between the repurchase rate of different products and the factors that strongly influence the repurchase rate in the case. To predict the sales of goods, the advantages of the BP neural network, LSTM neural network, and Verhulst gray model are combined: BP neural network can predict sales by combining the current data corresponding to independent variables, LSTM neural network can explore the influence of historical data on sales, and Verhulst model can predict sales based on the growth trend of variables. A BP-LSTM-Verhulst nested neural network based on the AP algorithm is constructed to predict sales volume, and the accuracy of this method is proved by example. Finally, it is found that the proposed sales prediction method has higher accuracy than exponential regression and shallow neural networks. The deep learning prediction method combining unstructured data such as images proposed in the paper not only provides a more accurate sales prediction method for short life cycle products in e-commerce but also provides an effective deep learning method for management practices. The KMO value obtained after the test is less than 0.5, which is not suitable for factor analysis. Using SPSS22.0 to expand the KMO value and Bartlett's sphericity test, the KMO value is significantly greater than the minimum requirement of 0.5, the KMO value of self-efficacy is 0.826, and the KMO value of expected return is 0.870.

Neural Network Model of Dynamic Prediction of Cross-Border E-Commerce Sales for Virtual Community Knowledge Sharing.

The current popular one with forecasting method simply studies for prediction, and insufficient consideration is given to the prediction of the evolution of product sales applied to Internet platforms. To improve the forecast effect and to realize the usage of the forecasting in line with “Internet+” surroundings, the product sales controllable correlation mining, personalized forecasting ways of counting, improve counting, and other corresponding algorithms, a “Internet + foreign trade” concept based on the controllable correlation comes up with the model of mobile prediction. The result can show that the sample has the opening features and dynamics of “Internet+” to prerealize the dynamic, intelligent, and quantitative qualitative prediction of export product sales based on the controllable correlation big data of cross-border e-commerce in the “Internet + foreign trade” environment. The comprehensive prediction effect of this model is obviously better than that of traditional models and has strong evolution and high practical value. This thesis has the benefits for promoting the technological development of cross-border e-commerce and making us cross the cross-border e-commerce industry.

RETRACTED: Gender Differences in Virtual Community Knowledge Sharing

Over the years, Web 2.0 and Web 3.0 have promoted and prospered user-generated content, ease of use, interoperability, and virtual communities. Indeed, a growing number of online platforms and virtual communities contribute to our society and economy by maximally sharing knowledge among numerous participants. Hence, it is necessary to understand the participation-motivation of knowledge sharing in various virtual communities. Using a sample of American virtual communities of interest, this study examines a model of knowledge sharing based on social capital theory and social cognitive theory. This research echoes prior studies with similar and even stronger evidence. Also, the results suggest a significant moderating effect of gender difference on knowledge sharing in virtual communities when participants share a common language and vision.

Quality decisions from physicians’ shared knowledge in virtual communities

ABSTRACT Physicians strive to reconcile decisions with social capital (SC) within virtual communities explained by Social Capital Theory (SCT)’s dimensions – identity, social interaction ties, and shared language. This study’s model with six hypotheses, assesses trust’s moderation between SC and knowledge sharing. Knowledge sharing enhances the quality of decision-making. A questionnaire got 204 response from US virtual community physicians. Confirmatory Factor Analysis and Structural Equation Modelling revealed that identity negatively influences shared language, interaction ties cannot influence knowledge sharing, though this behaviour enhances the quality of decision-making. Trust positively influences identity and quality in shared knowledge, but negatively influences this behaviour and shared language. Quality in shared knowledge partially mediates identity and quality of decision-making and fully mediates shared language and quality of decision-making. Theoretical and practical implications are reported in this first of its kind study assessing trust moderation and knowledge sharing mediation between SCT and quality of decision-making.

Enhancing information security best practices sharing in virtual knowledge communities

PurposeSharing information security best practices between experts via knowledge management systems is valuable for improving information security practices, exchanging expertise, mitigating security risks, spreading knowledge, reducing costs and saving efforts. The purpose of this paper is developing a conceptual model to enhance the transfer of information security best practices between professionals in virtual communities through a Web-based knowledge management system to exchange their successful experience in handling different information security situations.Design/methodology/approachThe model is validated by surveying 17 experts’ reviews on the correctness of the model’s structure and its related components through applying deep rich peer debriefing to test suitability. Quantitative data has been collected to achieve confirmatory results.FindingsThe resulting model incorporates five main components that support the formal mechanism for the acquisition and dissemination of knowledge: identification, classification, storage, validation and sharing. The success of knowledge sharing is highly dependent on the active collaboration of community members and highly influenced by motivation. Validating transferred knowledge is vital for ensuring the credibility of the system.Originality/valueTo the best of the author’s knowledge, this paper is one of the first to highlight the role of integrating knowledge management to enhance the effective share and reuse of information security best practices knowledge. The research results can support researchers investigating the topic and generate trustworthy literature to guide information security virtual community developers.

Physicians social capital aids their medical decisions when they virtually share knowledge

Knowledge sharing is daunting to physicians with unclear benefits of improving diagnoses/reducing medical errors. This study explores one aspect of shared knowledge sharing by assessing the effect of physicians' social capital on their decision making mediated by shared knowledge in virtual communities. This study followed a deductive approach. It quantitatively tested 14 hypotheses via a framework based on an adapted survey. This survey was distributed to 204 VC-physician-members. Empirical evidence suggested social capital positively influences physicians' decision making, through trust and identity, during the virtually shared knowledge. Theoretical and practical implications are discussed in our article. The model of our study is the first of its kind: holistically assesses the role of social capital theory, knowledge sharing, and decision making of virtual community physician members. Empirical findings extend current methods and understanding of the role of virtual community physicians' social capital of resources for decision making when mediated by their knowledge sharing behaviour.

A Calculus of Virtual Community Knowledge Intentions: Anonymity and Perceived Network-Structure

ABSTRACTThis study investigates the underlying motivational factors of knowledge exchange intentions (intention to obtain and to provide knowledge) within virtual community contexts. Perceived virtual network structures, namely virtual network connectivity (CN) and virtual network closeness (CL), are suggested as the important antecedents of knowledge sharing intentions in the context of virtual knowledge exchange communities. Anonymity (AN), one of the unique characteristics of virtual communities, but controversial due to its multi-faceted effects, is considered in a structural model as a factor having an impact on a virtual network structure. Data collected from participants of virtual communities through online surveys are analyzed using Partial Least Squares (PLS) structural equation modeling (SEM) to empirically test the proposed hypotheses. The results reveal that both CN and CL have a significant impact on both of the knowledge exchange intentions although CL shows an opposite direction of the impact. The results also show that AN has a significant impact on CL as expected but not on CN. Implications of this study may shed some light on better understanding community participants’ intentions to obtain and provide knowledge, along with the impact of anonymity on the perceived network structure.

Creating the sustainable conditions for knowledge information sharing in virtual community.

IntroductionEncyclopedias are not a new platform for the distribution of knowledge, but they have recently drawn a great deal of attention in their online iteration. Peer production in particular has emerged as a new mode of providing information with value and offering competitive advantage in information production.Case descriptionLarge numbers of volunteers actively share their knowledge by continuously editing articles in Baidu encyclopedias. Most articles in the online communities are the cumulative and integrated products of the contributions of many coauthors.Discussion and EvaluationEmail-based surveys and objective data mining were here used to collect analytical data. Critical mass theory is here used to analyze the characteristics of these collective actions and to explain the emergence and sustainability of these actions in the Baidu Encyclopedia communities.ConclusionsThese results show that, based on the collective action framework, the contributors group satisfied the two key characteristics that ensure the collective action of knowledge contribution will both take place and become self-sustaining. This analysis not only facilitates the identification of collective actions related to individuals sharing knowledge in virtual communities, but also can provide an insight for other similar virtual communities’ management and development.

How to unite the power of the masses? Exploring collective stickiness intention in social network sites from the perspective of knowledge sharing

This study aims to investigate users’ knowledge-sharing intention and collective stickiness intention towards social network sites (SNS). SNS offer an opportunity for users to interact and form relationships, while knowledge is accrued by integrating users’ information, experience, and practice. However, there have been few systematic studies that question why people use SNS to share knowledge. We adopt social capital theory, social identity theory, as well as use and gratification theory, to explore the determinants of members’ knowledge-sharing intention in using SNS. The survey was conducted on two education virtual communities (VCs) on Facebook, of which most members were teachers and educators. Data analysis was executed to validate our research model, and SmartPLS was used to analyse users’ collective stickiness intention. The results show that social capital and social identity have an impact on teachers’ knowledge-sharing intention, which, in turn, influences collective stickiness intention towards SNS. Our findings can not only help researchers interpret why members share their knowledge in VCs, but can assist practitioners in developing better SNS strategies as well.

Knowledge sharing in virtual communities: A social exchange theory perspective

Purpose: The author tried to identify the knowledge sharing behaviors on the internet, using structural equation modeling methods, proposing a model based on social exchange theory in which share willingness, trust, reciprocity, altruism tended to have impact on people’s knowledge sharing behaviors in virtual communities. Design/methodology/a pproach: We presented an empirical research which integrated social exchange theory and structural equation modeling methods to analyze several important factors influencing members’ knowledge sharing behaviors in virtual communities. Findings: We analyzed the knowledge sharing behaviors in virtual communities. We found that members’ altruism can not predict knowledge sharing behaviors. We also found that members’ sharing willingness is the most important factor on virtual community knowledge sharing behaviors compared with trust, reciprocity and altruism. Originality/v alue: From the perspective of social exchange theory, we did empirical test and verified the proposed research model by using structural equation modeling methods. Our finding can help recognize people’s incentive about knowledge sharing.

Discovering and Analyzing Methods on User-innovation Knowledge in Virtual Communities Based on Weighted Knowledge Network

Discovering and Analyzing Methods on User-innovation Knowledge in Virtual Communities Based on Weighted Knowledge Network

A virtual community question and answer suitability determination model

In this information-explosion era, the quality of collective intelligence is not easy to control. Knowledge demanders are required to invest an amount of time in evaluating the collective intelligence quality of virtual community to acquire correct knowledge. In addition, some knowledge providers might provide poor quality information in this circumstance and overall collective intelligence quality would be affected. The knowledge providers would reduce the willingness to share knowledge. Therefore, this paper develops a virtual community question and answer suitability determination (QASD) model to enhance collective intelligence quality. This proposed model analyzes the Q&A title, Q&A content, and the discussion and employs keyword extraction, article’s core information extraction, article similarity determination and semantic analysis techniques to determine the quality of Q&A, the suitability of Q&A content, and the suitability of Q&A semantics. After that, the integrated suitability score between target question and answer can be obtained and regarded as the reference for knowledge demanders in acquiring correct knowledge in virtual community. In order to demonstrate applicability of the proposed methodology, a web-based system is also established based on the proposed model. Furthermore, a real-world case is applied to evaluate the proposed model. The verification results show that when the system maintains about 600 training data, the indicator performance can be improved to 80% (Recall Rate) and 85% (Accuracy Rate), and the system performance continues to grow with increasing training amount and reaches the stable and better performance level. Hence, the developed system is a high-performance Q&A suitability determination system. As a whole, this paper provides an approach for virtual community to efficiently evaluate the suitability of Q&A.

Knowledge Sharing in Virtual Community Based on RDF Triple Publication and Retrieving To Process SPQRQL Query

Web ontologies, which usually appear as RDF graphs, are used to represent knowledge in web. In recent years, with the wide application of Semantic Web, large numbers of such web ontologies have appeared on the Internet, especially, in some virtual knowledge communities. These web ontologies are usually distributed in different sites and provide an amount of knowledge to query. But, it has become a pressing issue that, given a semantic query, how to efficiently gather the related knowledge from these web ontologies located in different sites to process it. To address this issue, in this paper, we propose a P2P-based approach to publish RDF triples in sharable web ontologies and freely sharing them in an open distributed environment. Given a query of SPARQL (Simple Protocol and RDF Query Language), this approach can automatically gather published RDF triples related to the query to process the query. As a knowledge sharing approach, our approach can directly share RDF triples coming from different web ontologies on different nodes. It overcomes limitations of those approaches which focus on how to locate related ontologies for a query. We also conducted three experiments to evaluate the effectiveness and the efficiency of our approach. The experimental results demonstrated that it is effective and efficient.

Why people share knowledge in virtual communities?

PurposeThe purpose of this paper is to answer the question: why Google Answers and Yahoo! Kimo Knowledge+, both virtual communities built on users asking and answering questions with different rewarding mechanisms, have different outcomes.Design/methodology/approachBased on the theory of reasoned action (TRA), the authors developed the constructs, including self‐efficacy, altruism, reward, and the sense of virtual community, that influence the intention of sharing knowledge in terms of answering questions on Knowledge+.FindingsThe results show that users showing higher levels of contribution tended to be motivated by virtual rewards, such as advanced ranks, and the need for self‐fulfillment. Additionally, for these knowledge providers, altruism is also an important factor. Therefore, these users share not because of a reward but because of altruism and fulfillment. The findings can answer why Google Answers failed with its monetary rewards but Knowledge+ remains with its virtual rewarding mechanism.Research limitations/implicationsThis study extends the literature on understanding the antecedents of sharing knowledge in terms of answering others’ questions in virtual communities. Especially, it identifies different factors affecting the intention of users in different levels of engagement with the community to share knowledge.Practical implicationsThe various effective factors influencing users’ knowledge sharing behavior identified in this study can guide the incentive mechanism design for virtual communities.Originality/valueBesides proposing research models to identify the constructs affecting the users’ intention to answer questions in a virtual community, such as Knowledge+, this study compares the models explaining the intention to share knowledge in different user groups with different levels of knowledge contribution. This research design is unique from the prior literatures; Moreover, the results shed light on designing incentive mechanisms for knowledge sharing in virtual communities.

Decision Making Modeled as a Theorem Proving Process

The authors introduce a theoretical framework enabling to process decisions making along some of the lines and methodologies used to mechanize mathematics and more specifically to mechanize the proofs of theorems. An underlying goal of Decision Support Systems is to trust the decision that is designed. This is also the main goal of their framework. Indeed, the proof of a theorem is always trustworthy. By analogy, this implies that a decision validated through theorem proving methodologies brings trust. To reach such a goal the authors have to rely on a series of abstractions enabling to process all of the knowledge involved in decision making. They deal with an Agent Oriented Abstraction for Multiagent Systems, Object Mechanized Computational Systems, Abstraction Based Information Technology, Virtual Knowledge Communities, topological specification of knowledge bases using Logical Fibering. This approach considers some underlying hypothesis such that knowledge is at the heart of any decision making and that trust transcends the concept of belief. This introduces methodologies from Artificial Intelligence. Another overall goal is to build tools using advanced mathematics for users without specific mathematical knowledge.