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Since stock return and volatility matters to investors, this study proposes to incorporate the textual sentiment of annual reports in stock price crash risk prediction.

Specific sentences gathered from management discussions and their subsequent analyses are tokenized and transformed into numeric vectors using textual mining techniques, and then the Naïve Bayes method is applied to score the sentiment, which is used as an input variable for crash risk prediction. The results are compared between a collection of predictive models, including linear regression (LR) and machine learning techniques.

The experimental results find that those predictive models that incorporate textual sentiment significantly outperform the baseline models with only accounting and market variables included. These conclusions hold when crash risk is proxied by either the negative skewness of the return distribution or down-to-up volatility (DUVOL).

It should be noted that the authors' study focuses on examining the predictive power of textual sentiment in crash risk prediction, while other dimensions of textual features such as readability and thematic contents are not considered. More analysis is needed to explore the predictive power of textual features from various dimensions, with the most recent sample data included in future studies.

The authors' study provides implications for the information value of textual data in financial analysis and risk management. It suggests that the soft information contained within annual reports may prove informative in crash risk prediction, and the incorporation of textual sentiment provides an incremental improvement in overall predictive performance.

The combination of mobile devices and innovative tools offers new possibilities for the development of a community of practice for design makers. Mobile learning has become an essential method that design makers should adopt. The main content of this study is to explore the characteristics of learning behaviors and learning needs of creative design makers' group in forming a community of practice in the era of mobile learning.

This study conducted questionnaire research on the potentially associated or directly associated population of design makers. The process of the study also combined observational and interview studies to compensate for the lack of questionnaire research.

Based on the support of mobile learning technology, design makers share and co-create to achieve individual development and evolution of learning organizations, and produce creative value. Design-maker communities of practice form common communities in the framework of informal organizations to support continuous individual learning. Convergent interests or concerns in making things, real-world contexts based on makerspaces and hands-on practice based on real projects are the basis for forming design-maker communities of practice. A variety of open-source hardware, software and platforms that can support mobile learning are important for the development of design-maker communities of practice. The design-maker community of practice needs group factors, activity development, physical and technical resources, spatial support and institutional norms to enhance learning behaviors and satisfy learning needs.

The discovery and construction of these associated factors can help creative design practitioners form a lasting and virtuous organizational development. This study facilitates the formation of a social network for learning and knowledge sharing among design-maker communities of practice. It enhances the innovation ability and enthusiasm of design makers according to the population characteristics and learning needs of design makers. This study also facilitates the generation of a positive adaptive maker culture and maker spirit within design maker organizations.

This study aims to propose a conceptual framework to examine the impact of risk management implementation on green innovation in the Malaysian solar photovoltaic (PV) manufacturing industry.

The study is based on primary data to be collected from 30 Malaysian solar PV manufacturing companies through a questionnaire that incorporates the five-point Likert scale. The exploratory factor analysis (EFA) is proposed to be performed using SPSS 24.0 and confirmatory factor analysis (CFA) is suggested to be conducted using AMOS.21 software to explore the factors and reliability of the items and to confirm the factorial structure of risk management implementation and green innovation. Furthermore, partial least square-structural equation modeling (PLS-SEM) is proposed to investigate relationships between constructs and latent variables.

The proposed framework is based on the stakeholder's theory and suggests that the comprehensive implementation of risk management has a significant and positive impact on green innovation in the Malaysian solar PV manufacturing industry.

This study provides insight into formulating strategies for enhancing green innovation in the solar PV manufacturing sector and serves as a valuable resource for stakeholders.

The significance of the proposed conceptual framework lies in its ability to enhance the workability of the stakeholder's theory and to create value for stakeholders through the implementation of risk management to drive green innovation. This study adds to the existing literature by exploring the relationship between risk management and green innovation in the solar PV manufacturing industry.