PurposeThe number of online communities has increased rapidly with the development of the Internet. Although these communities provide users with knowledge-sharing channels, the enthusiasm of the users for participation remains low. Ecosystem theory, social cognitive theory, and some other theories emphasize the importance of environmental factors and hypothesize that the behavior of users is affected by personal factors and environmental factors. In this study, the authors investigated the impact of platform environmental factors on knowledge-sharing behavior and improved the platform environment construction of online communities.Design/methodology/approachFirst, the authors analyzed the influencing factors of online community knowledge-sharing from the perspective of the platform environment, constructed a research model, and proposed relevant hypotheses. Then, the authors designed a questionnaire and conducted a survey, and finally tested the hypotheses by using the method of structural equation model.FindingsThe results showed that community trust, community management, community incentive, community atmosphere, and community information protection had a significant positive impact on community knowledge-sharing behavior. Community trust played a significant intermediary role in the relationship between community management, community incentive, community information protection, and knowledge-sharing behavior; community information protection only affected knowledge-sharing behavior through community trust. Additionally, community atmosphere did not directly affect knowledge-sharing behavior through the intermediary of community trust.Practical implicationsThis study showed that theoretical supplements and practical guidance are significant for conducting research and managing knowledge-sharing in the online community. They help community managers pay more attention to the construction of an online community platform environment.Originality/valueIn this study, the authors analyzed the influencing factors of online community knowledge-sharing from the perspective of the platform environment and introduced community trust as an intermediary variable. The research perspective and model of this study are novel to some extent.
Traditional online communities suffer from false, repetitive or low-level content, with blockchain technology able to solve these problems. Specifically, the incentive mechanism is the blockchain’s core value, including positive and negative incentive mechanisms. The former strengthens people’s behaviour positively, while the latter, on the contrary, adopts mandatory methods such as punishment to eliminate the occurrence of certain types of behaviour. The negative incentive mechanism is the key factor to solve the problems presented above that traditional online communities face. Specifically, this article develops a solution that utilises the negative incentive mechanism, based on the classic infectious disease model (SIR model), introduces smart nodes, puts forward the SSIR model of information dissemination in the blockchain network community, and establishes a set of differential equations reflecting the information dissemination rules. Based on the parameter assumption and solving the equations with MATLAB, this article compares and reveals the changes of different user types on the SIR and SSIR models. Furthermore, we utilise the data collected from the Steemit blockchain community and Sina Weibo platform and apply the Social Network Analysis method to compare and analyse the information dissemination between the blockchain and the traditional network community. The research results highlight that the negative incentive mechanism in the blockchain network community affords a more rational behaviour of user information dissemination, a simpler interaction between users, and reducing to a certain extent the dissemination of ‘distorted’ or ‘uncertain’ information.
Background: With the development of network technology and the continuous increase in user-generated content on the Internet, the protection of digital copyright is particularly important. Objective: In order to better protect the copyright of digital works, this paper proposes a Polkadot scheme based on the advantages of blockchain cross chain technology. Methods: The scheme sets up three parallel chains, including digital works chain, copyright management chain, and dispute arbitration chain, and shows the information interaction process of Validators, Fishermen, Collators, and Nominators. Results: The research shows that the scheme has more advantages than single chain or alliance chain. It has a shorter block creation time, better security and availability. Conclusion: The findings provide new ideas for copyright registration, copyright trading, and infringement maintenance of digital works, and also broadens the application of cross chain technology of blockchain.
Nowadays, the fast development of hardware for IoT-based systems creates appropriate conditions for the development of services for different application areas. As we know, the large number of multifunctional devices, which are connected to the Internet is constantly increasing. Today, most of the IoT devices just only collect and transmit data. The huge amount of data produced by these devices requires efficient and fast approaches to its analysis. This task can be solved by combining Artificial Intelligence and IoT tools. Essentially, AI accelerators can be used as a universal sensor in IoT systems, that is, we can create Artificial Intelligence of Things (AIoT). AIoT can be considered like a movement from data collection to knowledge aggregation. AIoT-based systems are being widely implemented in many high-tech industrial and infrastructure systems. Such systems are capable of providing not only the ability to collect but also analyse various aspects of data for identification, planning, diagnostics, evaluation, monitoring, optimization, etc., at the lower level in the entire system's hierarchy. That is, they are able to work more efficiently and effectively by generating the knowledge that is needed for real-time analytics and decision-making in some application areas.
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