Chinese firms have been widely seen as imitative. This historical case study explores what organizational mechanisms allowed Tencent, a Chinese firm in the fast-changing instant messaging (IM) service sector, to achieve a new-to-the-world innovation with its WeChat smartphone app. Tracing the competitive dynamics in the Chinese IM sector from its inception, we found that Tencent was able to create the innovative WeChat product through a crisis-induced intrafirm coopetition dynamic that was embedded in variation-selection-retention evolutionary processes spanning the market, the firm, and the business unit levels. Building on the intrafirm coopetition and evolutionary literatures, the paper shows that three business units simultaneously competed and cooperated in developing alternative IM products while being exposed to market selection for survival. The coopetition dynamic took place in three key areas: technology, product promotion, and complementary assets of suppliers. The relative balance between competition and cooperation changed over time, and top management guidance and firm-level routines were essential in managing the challenges of coopetition within the firm.

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User-generated contents (UGCs) on social media are a valuable source of emergency information (EI) that can facilitate emergency responses. However, the tremendous amount and heterogeneous quality of social media UGCs make it difficult to extract truly useful EI, especially using pure machine learning methods. Hence, this study proposes a machine learning and rule-based integration method (MRIM) and evaluates its EI classification performance and determinants. Through comparative experiments on microblog data about the “July 20 heavy rainstorm in Zhengzhou” posted on China’s largest social media platform, we find that the MRIM performs better than pure machine learning methods and pure rule-based methods, and that its performance is influenced by microblog characteristics such as the number of words, exact address and contact information, and users’ attention. This study demonstrates the feasibility of integrating machine learning and rule-based methods to mine the text of social media UGCs and provides actionable suggestions for emergency information management practitioners.

With the arrival of the new media era and the continuous development of China’s domestic IP dramas, the downturn in domestic TV dramas has been broken, more and more IP dramas obtain higher audience rating rely on the Internet, and it becomes a phenomenon hot style. Vietnam is geographically adjacent to China, and they have similar cultures. A lot of Vietnamese culture is derived from Chinese culture, and they are heavily influenced by the values and lifestyle of Confucius in the past, Chinese film and television is a literary genre familiar to Vietnamese, which is one of the most important components of daily entertainment. This paper will research on how to use the Internet to strengthen the import of film and television culture to Vietnam, so that Chinese IP dramas can also increase the audience rating in Vietnam and enhance the cultural export.