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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 tech giants Alibaba and Tencent compete on many fronts. This case focuses on three areas where they have competed very hard: new retailing, mobile payment, and ride-hailing. At the beginning of 2018, Alibaba and Tencent were gathering retail investments in bids to battle each other for shoppers' digital wallets. Key to the battle is China's mobile payment market, worth more than 200 trillion RMB, where Alibaba and Tencent are going head to head. The giants are not only directly competing in the payment platform area but also extensively fighting in other areas, such as ride-hailing, where they invested in and supported Didi and Kuaidi, respectively. To enhance understanding, this case also briefly goes through the history of the two giants. The purposes, methods, and consequences of their platform competition deserve an in-depth discussion

The goal of this paper is twofold: (1) to investigate how relatedness-supportive corporate social responsibility (CSR) initiatives influence brand happiness among retail bank customers through a mediating mechanism of customer participation in brand CSR movements; and (2) to analyze how relatedness-supportive CSR initiatives’ effect may be moderated by cause choice and customer-brand goal congruence.

Data were collected from 379 retail bank customers via a paper-and-pencil survey. The hypothesized moderated-mediation effects were tested using Hayes’ (2013) PROCESS (Model 3, Model 4 and Model 7).

Results show that relatedness-supportive CSR initiatives increase brand happiness among retail bank customers through increasing their participation in brand CSR movements. Furthermore, the use of customer determination in the choice of cause enhances the positive effect of relatedness-supportive CSR initiatives on customer participation in brand CSR movements. Similarly, when customers choose the cause and the customer-brand goal is congruent, the effect of relatedness-supportive CSR initiatives on brand happiness is stronger than when the customer-brand goal is incongruent and cause choice is not aligned.

This research is grounded on the relationship motivation theory (RMT), basic psychological needs theory and self-congruity theory to unpack the relationship between relatedness-supportive CSR programs on brand happiness. Integrating three research streams (i.e. CSR, brand management and retail banking), this study proposes customer participation in brand CSR movements as a novel mechanism and sheds light on how relatedness-supportive CSR interplays with cause choice/customer-brand goal congruence to affect brand happiness among retail bank customers in emerging markets.

This research investigates Hong Kong academic librarians' attitudes toward robotic process automation (RPA) and their willingness to learn this technology.

This qualitative study collected data through one-on-one semi-structured interviews conducted with video conferencing software. After participants received basic RPA information and three existing library application cases, they answered questions based on the interview guide. This research used the inductive thematic analysis method to analyze the collected data.

Regarding Hong Kong academic librarians' attitudes towards RPA, 19 themes were identified. Although all participants did not have previous knowledge of RPA, most showed positive attitudes toward implementing RPA in their libraries and some willingness to learn it. Besides, among all identified themes, negative attitudes mainly comprised “Affect” and “Cognition” factors, hindering RPA deployment in academic libraries.

This research helps librarians and RPA vendors make better decisions or strategies for implementing RPA for libraries, which has not been explored, especially in East Asia.