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WeChat is the largest social instant messaging platform in China, with 1.1 billion monthly active users. “Top Stories” is a novel friend-enhanced recommendation engine in WeChat, in which users can read articles based on preferences of both their own and their friends. Specifically, when a user reads an article by opening it, the “click” behavior is private. Moreover, if the user clicks the “wow” button, (only) her/his direct connections will be aware of this action/preference. Based on the unique WeChat data, we aim to understand user preferences and “wow” diffusion in Top Stories at different levels. We have made some interesting discoveries. For instance, the “wow” probability of one user is negatively correlated with the number of connected components that are formed by her/his active friends, but the click probability is the opposite. We further study to what extent users’ “wow” and click behavior can be predicted from their social connections. To address this problem, we present a hierarchical graph representation learning based model DiffuseGNN, which is capable of capturing the structure-based social observations discovered above. Our experiments show that the proposed method can significantly improve the prediction performance compared with alternative methods. [ABSTRACT FROM AUTHOR]

Purpose: This article aims to provide an integrated model and methodology for discovering valuable innovation knowledge and creative users in online innovative communities. Design/methodology/approach: The super-network integration approach is applied in constructing the user's innovation knowledge super-network (UIKSN) model based on knowledge fragments discovered from user-generated content (UGC) data using text mining methods. The social network analysis (SNA) methodology is then used to analyze the KSN model. An empirical research is conducted with China's Xiaomi online community to illustrate the UIKSN model and the SNA methodology. Findings: Compared with current methods, the proposed KSN model and SNA methodology are demonstrated to be more effective and valid in discovering valuable innovation knowledge including valuable innovations, innovation trends, creative users and their knowledge background, etc. Research limitations/implications: The results of KSN modeling and analysis are inevitably influenced by the performance of text mining methods. Practical implications: More and more companies start to adopt online community user's innovations in their new product developments. However, it is difficult to discover users’ opinions and innovations from the UGC due to its tremendous volume. Therefore, the KSN model and SNA methodology presented in this article are helpful for companies to manage online communities and to utilize users’ innovations effectively and efficiently. Originality/value: The article provides two main contributions in the study of online communities: 1) a well-justified model (users' innovation KSN, UIKSN) to explore online users’ contributions; 2) a new and more effective methodology to discover and analyze valuable innovations and innovative users. [ABSTRACT FROM AUTHOR]

The notion of proximity for innovation clusters needs to expand compared to traditional agglomeration literature because it involves multiple dimensions such as geography, business, and knowledge; however, limited research probed into this–it requires multidimensional data and novel methods. This article, therefore, proposes a three-layered framework that uses multisource heterogeneous data and network methods to measure the cluster-proximity in innovation clusters, in order to understand better the combined proximity between organizations within/across network communities. First, we developed a three-layered framework to map the network-based innovation cluster by integrating patent citation, business transaction, and geographic data. Second, in the innovation cluster, we identified the network communities in knowledge, business, and geographic layers, respectively. Third, we measured the cluster proximity within/across communities by using a combined index. We selected A City's machine tool sector in China as a case. This article found that machine-tool firms/organizations have higher cluster proximity within geographic communities that are enriched mostly by business connections. In comparison, they have lower cluster proximity across geographic communities, but the proximity is enhanced both by business connections and knowledge linkages. This may imply that knowledge linkages are more important in across-community proximity, and this needs more policy attention. [ABSTRACT FROM AUTHOR]

After entering the big data era, a new term of 'big knowledge' has been coined to deal with challenges in mining a mass of knowledge from big data. While researchers used to explore the basic characteristics of big data, we have not seen any studies on the general and essential properties of big knowledge. To fill this gap, this paper studies the concepts of big knowledge, big-knowledge system, and big-knowledge engineering. Ten massiveness characteristics for big knowledge and big-knowledge systems, including massive concepts, connectedness, clean data resources, cases, confidence, capabilities, cumulativeness, concerns, consistency, and completeness, are defined and explored. Based on these characteristics, a comprehensive investigation is conducted on some large-scale knowledge engineering projects, including the Fifth Comprehensive Traffic Survey in Shanghai, the China's Xia-Shang-Zhou Chronology Project, the Troy and Trojan War Project, and the International Human Genome Project, as well as the online free encyclopedia Wikipedia. We also investigate the recent research efforts on knowledge graphs, where they are analyzed to determine which ones can be considered as big knowledge and big-knowledge systems. Further, a definition of big-knowledge engineering and its life cycle paradigm is presented. All of these projects are accordingly checked to determine whether they belong to big-knowledge engineering projects. Finally, the perspectives of big knowledge research are discussed. [ABSTRACT FROM AUTHOR]

Abstract: This paper addresses the issues that arise when corporations are required to make strategic choices regarding operation in foreign countries with diverse socio-political systems and culture, while taking into consideration all the relevant stakeholders, which ultimately affects the corporations’ current and future payoffs. Using the game theory principles we focus on Google''s internet search operations in China, particularly information dissemination decisions, and we examine how Google''s decisions affect the corporation''s success on the Chinese market, which is additionally determined by the information regulation policy of the Chinese authorities. In view of the fact that goals of Google and Chinese authorities are partially overlapping and partially conflicting this perfect information game contains both competitive and cooperative elements. In this paper we will argue that for Google or any other company in the equal situation the best strategic choice would be to respect and follow the laws and restrictions set by the authorities of the country in which the company chooses to operate. Finally, an analysis is completed through determining the optimal strategy for Google and Chinese authorities and finding the equilibrium point. This work provides a new perspective for knowledge engineering. [Copyright &y& Elsevier]

University ties are an important innovation resource for both foreign direct investment (FDI) firms and local firms. Since these university connections involve local personal ties, which are established through long-term efforts that require social and cultural embeddedness, it is probably more difficult for FDI firms to establish such ties than it is for local firms. However, FDI firms, which have two advantages, size and R&D capacity, can possibly compensate for that disadvantage. This paper contributes to the current innovation and international business literature by comparing the effect of university ties on innovation between local firms and FDI firms due to their heterogeneous resources and capabilities. Specifically, this paper examines the joint influence of university ties, R&D capacity, and firm size in both FDI firms and local firms in China. The results show that R&D capacity and firm size have different moderating effects on FDI firms and local firms, suggesting that internal capability and external personal relationship with universities are substitutes in local firms but complementary in FDI firms. Our results are relevant for practicing managers because they show that acquiring the knowledge in universities is contingent on firm characteristics as well as ownership types. [ABSTRACT FROM PUBLISHER]