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Drawing on the stakeholder theory, this study aims to empirically analyse the impact of platform enterprises’ corporate social responsibility (CSR) announcements on corporate stock market value. This study also estimates the moderating effect of stakeholder orientation and responsibility categories of CSR announcements, the platform enterprise type and the degree of CSR disclosure.

The event study method is used to analyse the change in stock market value of 191 CSR announcements from 137 Chinese platform enterprises. In addition, a case analysis is presented for two platform enterprises with the best practices to validate and complement study findings.

CSR announcements improve platform enterprises’ stock market value. Specifically, CSR announcements responding to platform enterprises’ external stakeholders, and CSR announcements with economic responsibility, have obvious positive impacts on stock market value. Furthermore, the maker platform’s CSR announcement has a more positive impact on stock market value than the exchange platform.

To the best of the authors’ knowledge, this study is the first attempt to identify the link between platform enterprises’ CSR announcements and stock market performance by empirical evidence, and it contributes to new knowledge of operating and evaluating platform enterprises’ CSR.

In this paper the authors aim to argue that the existence of a strong corporate governance mechanism (a formal credibility-enhancing mechanism) and the presence of a more trustworthy-looking CEO (an informal credibility-enhancing mechanism) are substitutes.

By using machine-learning-based facial-feature-point detection technique, the authors construct a proprietary facial-trustworthiness database for a large-scale of CEOs in the US listed companies. First, the authors manually search for qualifying CEO image from websites and annual reports. Second, by following the neuroscience and psychology literature, the authors use the machine-learning-based face detector to identify the facial features in the CEO photos to calculate a rich and reliable set of facial-trustworthiness measures. The authors then construct a composite facial-trustworthiness index for each CEO. After obtaining accounting data, the authors’ final sample comprises 16,201 firm-year observations for 3,186 CEOs in the sample period of 2000-2018.

The results of the authors’ regression analyses show a negative association between board monitoring intensity and CEOs' facial trustworthiness, indicating that board directors may factor CEOs' facial trustworthiness into their monitoring decisions. Moreover, the authors find that these results are mainly driven by CEOs whose tenure is below the third quartile (i.e. eight years). The authors further find stronger results for externally hired CEOs than internally promoted CEOs. Finally, the authors’ results remain robust when using change models or subsample of CEO photos in recent years.

First, to the best of the authors’ knowledge, this is the first study that adopts a large sample to provide systematic evidence on the directors' use of facial trustworthiness. This study extends the literature by documenting the impacts of CEOs' individual characteristics on the board monitoring intensity. Second, the results of this study emphasized the important role of perceptions based on executives' facial appearance in firm valuation, executive compensation and audit fee, and by presenting empirical evidence that CEOs' facial trustworthiness affects board monitoring intensity. Third, this study responds to the call for research on personalized trust by Hsieh et al. (2020).

The rapid development of digital reading has made it a mainstream reading method for the public, and scholars have conducted research on its effectiveness.The purpose of this study is to systematically summarize and generalize the factors that affect the effectiveness of digital reading in current practical research.

Retrieved the search results from the Web of Science database and the China National Knowledge Infrastructure database, collected the relevant literature in both Chinese and English on the effectiveness of digital reading, qualitatively coded the relevant literature, and conducted a systematic literature review analysis on the factors affecting the effectiveness of digital reading.

There are 37 factors that influence the effectiveness of digital reading, forming five factor themes, namely, the reading subject, reading environment, organizational support, technical support and reading text. The five influencing factor themes are further divided into three types of functional mechanisms, namely, driving, supportive and assurance mechanisms. Based on this, a research framework is proposed, providing a comprehensive approach for the research positioning of digital reading effectiveness.

A research framework is proposed, providing a comprehensive approach for the research positioning of digital reading effectiveness.

Community question answering (CQA) platforms play a significant role in knowledge dissemination and information retrieval. Expert recommendation can assist users by helping them find valuable answers efficiently. Existing works mainly use content and user behavioural features for expert recommendation, and fail to effectively leverage the correlation across multi-dimensional features.

To address the above issue, this work proposes a multi-dimensional feature fusion-based method for expert recommendation, aiming to integrate features of question–answerer pairs from three dimensions, including network features, content features and user behaviour features. Specifically, network features are extracted by first learning user and tag representations using network representation learning methods and then calculating questioner–answerer similarities and answerer–tag similarities. Secondly, content features are extracted from textual contents of questions and answerer generated contents using text representation models. Thirdly, user behaviour features are extracted from user actions observed in CQA platforms, such as following and likes. Finally, given a question–answerer pair, the three dimensional features are fused and used to predict the probability of the candidate expert answering the given question.

The proposed method is evaluated on a data set collected from a publicly available CQA platform. Results show that the proposed method is effective compared with baseline methods. Ablation study shows that network features is the most important dimensional features among all three dimensional features.

This work identifies three dimensional features for expert recommendation in CQA platforms and conducts a comprehensive investigation into the importance of features for the performance of expert recommendation. The results suggest that network features are the most important features among three-dimensional features, which indicates that the performance of expert recommendation in CQA platforms is likely to get improved by further mining network features using advanced techniques, such as graph neural networks. One broader implication is that it is always important to include multi-dimensional features for expert recommendation and conduct systematic investigation to identify the most important features for finding directions for improvement.

This work proposes three-dimensional features given that existing works mostly focus on one or two-dimensional features and demonstrate the effectiveness of the newly proposed features.

This paper aims to present a method for improving the performance of the visual-inertial navigation system (VINS) by using a bio-inspired polarized light compass.

The measurement model of each sensor module is derived, and a robust stochastic cloning extended Kalman filter (RSC-EKF) is implemented for data fusion. This fusion framework can not only handle multiple relative and absolute measurements, but can also deal with outliers, sensor outages of each measurement module.

The paper tests the approach on data sets acquired by a land vehicle moving in different environments and compares its performance against other methods. The results demonstrate the effectiveness of the proposed method for reducing the error growth of the VINS in the long run.

The main contribution of this paper lies in the design/implementation of the RSC-EKF for incorporating the homemade polarized light compass into visual-inertial navigation pipeline. The real-world tests in different environments demonstrate the effectiveness and feasibility of the proposed approach.

Smartbi is a nascent Chinese BI services provider in such a competitive and promising market. CEO Huafu Wu is contemplating a revision of its sales strategy: whether to prioritize customized products for key accounts or offer standardized services to a wider market. The shift of sales strategy brings a relevant question regarding targeting large customers or reaching mid-sized customers through distributors. This case can be used in marketing courses to illustrate the interplay between markets and products, the distinction between horizontal and vertical market expansion, and the complexities of channel management.

This paper aims to study vehicle-type strategies for the manufacturer's car sharing by accounting for consumers' behavior and the subsidy.

The authors develop a game model, in which a monopoly manufacturer that can produce gasoline vehicles (GVs) or energy vehicles (EVs) not only sells vehicles in the sales market, but also rents them out in the sharing market by the self-built platform. The manufacturer strategically chooses which type of vehicles based on consumers' behavior and whether the government provides the EVs’ subsidy.

When consumers' low-carbon awareness is relatively high or the marginal cost is low, the manufacturer chooses EVs. The manufacturer chooses GVs when the low-carbon awareness and the marginal cost are low. Only when the low-carbon awareness and the subsidy are not too low, the manufacturer who originally chose GVs launches EVs. When the low-carbon awareness is high, the excessive subsidy discourages the manufacturer from entering the sharing market. If the government provides the subsidy, the manufacturer launches high-end EVs. Otherwise, the manufacturer launches low-end EVs. Moreover, the subsidy increases consumer surplus and social welfare since the high subsidy makes EVs’ sharing market demand be negative.

This study enriches the literature on vehicle-type strategies for the manufacturer's car sharing, owns a practical significance to guide the manufacturer's operation management in the car sharing market and provides advice on whether the government should provide EVs’ subsidy.

Drawing on affect-based mechanisms, this chapter describes two forms of customer mistreatment, aggressive and demanding mistreatment. Tests are conducted of their lagged effects in predicting within-person fluctuation of employees’ negative mood, as well as the moderating roles of employees’ emotion regulation after work (i.e., rumination and social sharing). 1,185 daily surveys were collected from 149 Chinese customer service representatives from a call center for eight weekdays. Results supported the main effects of both forms of customer mistreatment and partly supported the moderating roles of rumination in strengthening the impacts of customer mistreatment. Implications and limitations are discussed.

Coronavirus disease 2019 (COVID-19) has made telecommuting widely valued, but different individuals have different degrees of acceptance of telecommuting. This article aims to identify suitable individuals for telework and to clarify which types of workers are suitable for what level of telework, set scientific, reasonable hybrid work ratios and processes and measure their suitability.

First, two working scenarios of different risk levels were established, and the theory of planned behavior (TPB) was used to introduce latent variables, constructing a multi-indicator multi-causal model (MIMIC) to identify suitable individuals, and second, constructing an integrated choice and latent variable (ICLV) model of the working method to determine the suitability of different types of people for telework by calculating their selection probabilities.

It is possible to clearly distinguish between two types of suitable individuals for telework or traditional work. Their behavior is significantly influenced by the work environment, which is influenced by variables such as age, income, attitude, perceived behavioral control, work–family balance and personnel exposure level. In low-risk scenarios, the influencing factors of the behavioral model for both types of people are relatively consistent, while in high-risk scenarios, significant differences arise. Furthermore, the suitability of telework for the telework-suitable group is less affected by the pandemic, while the suitability for the non-suitable group is greatly affected.

This study contributes to previous literature by: (1) determining the suitability of different population types for telework by calculating the probability of selection, (2) dividing telework and traditional populations into two categories, identifying the differences in factors that affect telework under different epidemic risks and (3) considering the impact of changes in the work scenario on the suitability of telework for employees and classifying the population based on the suitability of telework in order to avoid the potential negative impact of telework.