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Public reviews on educational robots are of great importance for the design, development and management of the most advanced robots with an educational purpose. This study explores the public attitudes and emotions toward educational robots through online reviews on Weibo and Twitter by using text mining methods.

Our study applied topic modeling to reveal latent topics about educational robots through online reviews on Weibo and Twitter. The similarities and differences in preferences for educational robots among public on different platforms were analyzed. An enhanced sentiment classification model based on three-way decision was designed to evaluate the public emotions about educational robots.

For Weibo users, positive topics tend to the characteristics, functions and globalization of educational robots. In contrast, negative topics are professional quality, social crisis and emotion experience. For Twitter users, positive topics are education curricula, social interaction and education supporting. The negative topics are teaching ability, humanistic care and emotion experience. The proposed sentiment classification model combines the advantages of deep learning and traditional machine learning, which improves the classification performance with the help of the three-way decision. The experiments show that the performance of the proposed sentiment classification model is better than other six well-known models.

Different from previous studies about attitudes analysis of educational robots, our study enriched this research field in the perspective of data-driven. Our findings also provide reliable insights and tools for the design, development and management of educational robots, which is of great significance for facilitating artificial intelligence in education.

Although the literature has highlighted that a firm’s board is critical for firm innovation, the impact of board characteristics on firm innovation has always been examined separately, leading to inconclusive research results. Based on the complexity theory, this paper incorporates four board characteristics, including board size, board ownership, board independence and CEO duality, to examine the impact of the combinations of different board characteristics on firm innovation through qualitative comparative analysis.

Using the panel data of listed manufacturing firms in China from 2007 to 2022, this paper conducted the fuzzy set qualitative comparative analysis to test the proposed hypotheses.

The research results show that no single board characteristic can explain firm innovation, as board size, board ownership, board independence and CEO duality can lead to either positive or negative firm innovation. Moreover, firm innovation depends on a complex combination of board characteristics.

This paper makes the following contributions: Firstly, this paper advances the firm innovation literature by extending the role of board characteristics on firm innovation, thereby offering a new way to model firm innovation in terms of board characteristics. Secondly, this paper provides a more comprehensive account of the role of a firm’s board by integrating agency theory and resource dependence theory. Thirdly, this paper also identifies a promising avenue for further research in the field of corporate governance: the investigation of other contingency contexts in which the effect of board characteristics may be observed, with the aim of further increasing the understanding of board functioning.