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Purpose: Online open innovation platforms provide opportunities for product users to participate in the innovation process and contribute their ideas to the platform. Nonetheless, they also present a significant challenge for platform managers, who select high-quality innovations from a massive collection of information with diverse quality. Design/methodology/approach: In this study, the authors employed a machine learning method to automatically collect a real dataset of 2,276 innovations and 30,004 detailed comments from the online platform of IdeaExchange and then conducted empirical experiments to verify the study hypothesis. Findings: Results show that extraversion, conscientiousness and openness to experience positively and directly influenced the quality of their innovation. Furthermore, an individual's social network position mediated among extraversion, neuroticism, conscientiousness and openness to experience and the quality of an innovation. Research limitations/implications: Results showed that extraversion, conscientiousness and openness to experience positively and directly influenced the quality of their innovation. Furthermore, an individual's social network position mediated among extraversion, neuroticism, conscientiousness, openness to experience and the quality of innovations. Originality/value: This study combined the Big Five personality traits theory and social network theory to examine the association between user intrinsic personality traits, social network position and the quality of their innovative ideas in the context of online innovation platforms. Additionally, the findings provide new insights for platform managers on how to select high-quality innovation information by considering user personality traits and their social network position. [ABSTRACT FROM AUTHOR]

Pathogens mutate as diseases spread, and variants that become epidemic or pandemic strains have higher transmission rates and a greater capacity to escape vaccine protection. Considering different vaccine efficacy and vaccination rates is of great significance for the prevention and control of infectious diseases. The spread of vaccination information and the level of trust in vaccinations determine whether people will choose to be vaccinated. To analyze these factors, we developed mean‐field equations for system dynamics to model the complex system of vaccine information dissemination and vaccination, calculated the basic reproduction rate number, and used the Icelandic COVID‐19 outbreak (Omicron variant) as a case study. We found that in the face of emerging variants, increasing vaccine efficacy is more effective than increasing vaccination rates. If vaccine efficacy increases from 40% to 90%, infections can be decreased by 98.5%. However, even a 100% vaccination rate cannot stop the spread of a mutated virus if vaccine efficacy falls below a certain level. High vaccination rates decrease the virus transmission rate. If the efficacy of the vaccine diminishes, the infection will spread rapidly, leading to a greater number of individuals becoming infected with the infectious disease. Due to the high vaccine efficacy against major illnesses and fatalities, improving vaccination rates can lower deaths. Iceland could decrease deaths by 44.8% by raising the vaccination rate from 75.9% to 95.0%. To combat emerging virus variants, it is therefore necessary to both create more effective vaccines and raise awareness of the benefits of vaccination to increase vaccination rates. [ABSTRACT FROM AUTHOR]

The COVID-19 prevention and control measures are taken by China's government, especially traffic restrictions and production suspension, had spillover effects on air quality improvement. These effects differed among cities, but these differences have not been adequately studied. To provide more knowledge, we studied the air quality index (AQI) and five air pollutants (PM2.5, PM10, SO2, NO2, and O3) before and after the COVID-19 outbreak in Shanghai, Wuhan, and Tangshan. The pollution data from two types of monitoring stations (traffic and non-traffic stations) were separately compared and evaluated. We used monitoring data from the traffic stations to study the emission reduction caused by traffic restrictions. Based on monitoring data from the non-traffic stations, we established a difference-in-difference model to study the emission reduction caused by production suspension. The COVID-19 control measures reduced AQI and the concentrations of all pollutants except O3 (which increased greatly), but the magnitude of the changes differed among the three cities. The control measures improved air quality most in Wuhan, followed by Shanghai and then Tangshan. We investigated the reasons for these differences and found that differences in the characteristics of these three types of cities could explain these differences in spillover effects. Understanding these differences could provide some guidance and support for formulating differentiated air pollution control measures in different cities. For example, whole-process emission reduction technology should be adopted in cities with the concentrated distribution of continuous process enterprises, whereas vehicles that use cleaner energy and public transport should be vigorously promoted in cities with high traffic development level. [ABSTRACT FROM AUTHOR]

Microblog sentiment analysis has important applications in many fields, such as social media analysis and online product reviews. However, the traditional methods may be challenging to compute the long dependencies between them and easy to lose some semantic information due to low standardization of text and emojis in microblogs. In this paper, we propose a novel deep memory network with structural self-attention, storing long-term contextual information and extracting richer text and emojis information from microblogs, which aims to improve the performance of sentiment analysis. Specifically, the model first utilizes a bidirectional long short-term memory network to extract the semantic information in the microblogs, and considers the extraction results as the memory component of the deep memory network, storing the long dependencies and free of syntactic parser, sentiment lexicon and feature engineering. Then, we consider multi-step structural self-attention operations as the generalization and output components. Furthermore, this study also employs a penalty mechanism to the loss function to promote the diversity across different hops of attention in the model. This study conducted extensive comprehensive experiments with eight baseline methods on real datasets. Results show that our model outperforms those state-of-the-art models, which validates the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]