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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.

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.

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.

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.

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.

Large volumes of users' creative information have rapidly become vital resources in the open innovation platforms, so it is crucial to identify high-quality information from massive creative information. However, the existing literature on the quality of creative information only focuses on the information characteristics or publishers' features.

In this paper, the authors used the elaboration likelihood model to examine the joint effect of central route factors (information characteristics: timeliness, readability and sentiment) and peripheral route factors (source characteristics: personality traits, past successful experiences and social network location) on the quality of creative information. Furthermore, the author explored the moderating roles of companies' support between central and peripheral route factors on the quality of creative information. Finally, binary logistic regression was adopted to test the research hypotheses on the empirical data from Salesforce.

The results indicated that users with high extroversion, conscientiousness, social centrality and prior success rate tended to propose high-quality information. Meanwhile, information timeliness, readability and sentiment also negatively influence the quality of creative information.

Different from previous studies, the study findings not only provide insights on identifying the quality of creative information from an information perspective, but also promotes the awareness of the intrinsic personality traits of information users and innovative support efforts by platforms and their managers.

Intangible resources (IRs) play an important role in enterprise innovation; previous studies find inconsistent results (positive and negative). The authors develop and test a framework to analyze IRs to see whether and how to impact firm innovation performance to reconcile the conflicting results.

This study empirically examined the curvilinear effect of IRs and innovation performance (IP) based on data from the Annual Census of Chinese Industrial Enterprises. The moderating effect of institutional development (ID) and state ownership (SO) in the relationship between firms' IRs and IP was also examined.

It was found that there is a U-shaped relationship between IRs and IP. Moreover, the institutional development weakens the U-shaped relationship.

The U-shaped relationship explains the inconsistent results in previous studies. It offers some important implications for managers and policymakers, who must understand the role of IRs.