The acquisition of innovation performance through social network relationship resources is a common behavior pattern of organizational members. Recent social network research suggests that negative ties may also have a positive impact on organizational innovation compared with positive ties. Based on this, the paper investigates the impact of dissonant tie, which are a combination of problem-solving tie and difficult working tie, on organizational innovation. The empirical results show that dissonant tie promotes organizational innovation performance and are enhanced by digital sensing and digital seizing, while the increasing effect of the dissonant tie on organizational innovation performance is not verified under the moderating effect of digital reconfiguring. The findings are useful for understanding why certain negative ties may promote organizational innovation and performance, and to provide a theoretical basis for how manufacturing companies can use complex social network ties to survive organizational change and enhance organizational adaptability in digital transformation.

As the micro-foundation of the market economy, identifying and preventing various risks are crucial both for the companies themselves and for market regulators, financial institutions and investors. To detect enterprise risks, many studies have constructed and utilized various indicator metrics based on information about each company to assess the level of enterprise risk. However, enterprises are not independent in the market, they form a complex network on the basis of their production activities. The enterprise risk comes not only from the operation of each enterprise itself, but also from the related enterprises in its business network. In addition, the risk detection methods relied on indicator metrics aims to asset a specific or a certain kind of risk faced by enterprises. That is, these methods are incapability of identifying the constantly changing risks caused by the uncertainty of enterprises. To this end, this study aims to (i) construct a network of enterprises based on supply chain relationships to incorporate the contagion effect of related companies, (ii) learn event prototype representations from company action event data via a self-supervised learning method, and capture the time-varying operational states of enterprises using the learned prototypes, (iii) adopt graph neural networks to identify enterprise risks from the perspective of supply chain contagion and comprehensive events. The experimental results on real datasets show that the proposed method is important for predicting and assessing enterprise risks and provides useful information for the decision-making of companies and investors.

Media-aware stock performance has been well recognized in recent studies. Previous research, however, focused on the content influence of the media, ignoring the manner in which the media is delivered. Based on the trust theory, this study argues that the media platforms, as media distribution vehicles and trust endorsement for news, are themselves influential on the stock market. This paper collected news data from seven Chinese mainstream media platforms and classified them into official, professional, and mass media platforms to investigate the impact of different platforms. The authors find that high official and professional media coverage predict increased abnormal returns, while high mass media coverage predicts the opposite. In addition, this paper systematically explores the mechanism of media platforms on stock performance from the perspectives of platform content, audience, and publication timeliness. The findings include that investors' attention to media platforms has a moderating effect on the stock performance, and such an effect is more salient in bear markets.

In finance, it is believed that market information, namely, fundamentals and news information, affects stock movements. Such media-aware stock movements essentially comprise a multimodal problem. Two unique challenges arise in processing these multimodal data. First, information from one data mode will interact with information from other data modes. A common strategy is to concatenate various data modes into one compound vector; however, this strategy ignores the interactions among different modes. The second challenge is the heterogeneity of the data in terms of sampling time. Specifically, fundamental data consist of continuous values sampled at fixed time intervals, whereas news information emerges randomly. This heterogeneity can cause valuable information to be partially missing or can distort the feature spaces. In addition, the study of media-aware stock movements in previous work has focused on the one-to-one problem, in which it is assumed that news affects only the performance of the stocks mentioned in the reports. However, news articles also impact related stocks and cause stock co-movements. In this article, we propose a tensor-based event-driven LSTM model to address these challenges. Experiments performed on the China securities market demonstrate the superiority of the proposed approach over state-of-the-art algorithms, including AZFinText, eMAQT, and TeSIA.