Purpose This paper aims to clearly conceptualize the idea of the smart library and propose a holistic approach to building smart libraries, in accordance with recent practices and state-of-the-art technologies. Design/methodology/approach Drawing on an extensive review of existing literature and practice about library construction, this paper distinguishes between similar types of smart library and divides the concepts associated with smart library building into three dimensions: technology, service and human. Findings Traditional libraries can transform to smart libraries by strategic design and implementation of advanced technologies, such as cloud computing, data mining and artificial intelligence, but they also need to consider service building, user cultivation and librarian training. Originality/value Aligning to the three main dimensions of smart libraries (technology, service and human), this study clarifies the concept of the smart library and offers strategic principles: integration of infrastructures, construction of service and human learning. It provides guidelines and directions for public and academic libraries committed to becoming smart libraries.
The social Q&A community, as one of the most popular platforms of knowledge sharing, now faces user churn and slower growth in new active user numbers. To some extent, the main problem concealing in the decrease of growth is the lack of knowledge contribution from the users, which is the most attractive function of the social Q&A community. Thus the key point of tackling this problem is to explore which factors influence users' knowledge sharing behavior. Based on social capital theory and social exchange theory, this study constructs a regression model and collects empirical data to investigate the influencing factors of users' knowledge sharing behavior. It is found that the increment of users' centrality, harvest and users' trust toward the community lead to more and better shared‐knowledge. However, the increase in users' reputation has a significant negative impact on the quantity and quality of their behavior. The theoretical and practical implications derived from the findings are further discussed.
Purpose COVID-19, a causative agent of the potentially fatal disease, has raised great global public health concern. Information spreading on the COVID-19 outbreak can strongly influence people behaviour in social media. This paper aims to question of information spreading on COVID-19 outbreak are addressed with a massive data analysis on Twitter from a multidimensional perspective. Design/methodology/approach The evolutionary trend of user interaction and the network structure is analysed by social network analysis. A differential assessment on the topics evolving is provided by the method of text clustering. Visualization is further used to show different characteristics of user interaction networks and public opinion in different periods. Findings Information spreading in social media emerges from different characteristics during various periods. User interaction demonstrates multidimensional cross relations. The results interpret how people express their thoughts and detect topics people are most discussing in social media. Research limitations/implications This study is mainly limited by the size of the data sets and the unicity of the social media. It is challenging to expand the data sets and choose multiple social media to cross-validate the findings of this study. Originality/value This paper aims to find the evolutionary trend of information spreading on the COVID-19 outbreak in social media, including user interaction and topical issues. The findings are of great importance to help government and related regulatory units to manage the dissemination of information on emergencies, in terms of early detection and prevention.
In recent years, the B2C e-commerce achieved a rapid development on a global scope; more and more people began to use the Internet for shopping. However, the exponentially increasing information provided by Internet enterprises causes the problem of overloaded information, and this inevitably reduces the customer's satisfaction and loyalty. One way to overcome such problem is to build personalized recommender systems to retrieve product information that really interests the customers. The rapid development of Web 2.0 provides new ideas for personalized recommendation. In this paper we introduce the collaborative filtering, knowledge-based approaches and hybrid approaches in building recommender systems and discuss the strengths and weaknesses of each approach. we propose a collaborative tagging system to provide personalized product information to customers in B2C e-commerce websites and describe the system's architecture and point the system's advantage.
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