The role of ESG performance during times of COVID-19 pandemic.

The outbreak of the epidemic in 2020 has caused a huge negative impact on the production and operation of firms, directly threatening their survival and development. However, some firms can make timely and effective adjustments in the face of sudden crises because of their resilience, and then turn the corner. This study selects the data of 2993 companies listed in Chinese A shares. The OLS method and event study is used to analyze the impact of ESG on the ability of corporate system crisis (corporate resilience). The research results indicate that companies with good ESG performance are more resilient in crises. The mechanism test indicates that the easing effect of corporate financing constraints and the expansion effect of corporate green innovation capabilities are important channels for ESG performance to promote the negative impact of crisis shocks on corporate value. Heterogeneity analysis indicates that ESG has a stronger ability to respond to systemic crises in small-scale firms, state-owned firms, and highly competitive market environments. Powerful CEOs can weaken ESG's ability to respond to systemic corporate crises. Further research has found that only S and G items, namely good governance level and social performance, have a significant positive promoting effect on corporate resilience. ESG performance may be more important in areas more severely affected by the epidemic. This study expands the research on ESG and the research on the decision mechanism of enterprise resilience. This study provides a new theoretical perspective for the study of corporate crisis response capabilities, and provides a certain policy reference for Chinese firms to effectively respond to public crises, which has important policy implications.

The Digital Entrepreneurship Era: How to Motivate Innovativeness in Middle Management Teams? The Vertical Organisational Pervasiveness of Chief Executive Officer Entrepreneurial Orientation.

Social information processing theory suggests that the chief executive officer's entrepreneurial orientation (CEO EO) is an organisational signal that influences the members' innovativeness. Middle management teams (MMTs) are expected to be more innovative as they connect senior managers with frontline managers in the dynamic competitive environment of the digital economy. How CEOs guide MMT innovations through EO becomes critical in the process of capturing opportunities and creating value. However, previous research has failed to adequately identify distinct CEO EO manifestations with organisational contexts configurations that influence MMTs innovation. Thus, based on differences in organisational contexts and MMTs' cognition, this study thoroughly investigates how the vertical manifestation of CEO EO impacts the innovativeness of MMTs. We used fuzzy-set qualitative comparative analysis (fsQCA) on a sample of 117 organisations to determine which configurations of CEO EO vertical penetration within an organisation can stimulate MMT innovativeness. The study discovered four first-level configurations that support stimulating MMT innovativeness respectively when the CEO EO is fully or partially manifested, and without the CEO EO. Moreover, we found the internal reasons for MMTs' information interpretation heterogeneity, which is critical for realising the coordination and unity of entrepreneurial cognition and behaviours. Finally, these findings' theoretical and practical implications are discussed.

Chief Executive Officers Entrepreneurial Orientation, Dynamic Capabilities, and Firm Performance: The Moderating Effect of the Manufacturing Industry.

This study explores the implications of CEO entrepreneurial orientation for firm performance through corporate dynamic capabilities. It explores the moderating effects of firm industry type on the above indirect effect. Based on 188 matched sample data collected from vice chief executive officers (CEOs) of Chinese firms, this study found that CEO entrepreneurial orientation was positively related to corporate dynamic capabilities and firm performance and that corporate dynamic capabilities mediated the positive relationship between CEO entrepreneurial orientation and firm performance. Firm industry type moderated the direct effect of CEO entrepreneurial orientation on corporate dynamic capability, and the indirect effect of CEO entrepreneurial orientation on firm performance through corporate dynamic capability. Both direct and indirect effects were stronger in manufacturing enterprises. The findings enrich the CEO entrepreneurial orientation literature by extending the existing knowledge on its underlying mechanism and its impact on firm performance, as well as its boundary conditions.

Prediction in Autism by Deep Learning Short-Time Spontaneous Hemodynamic Fluctuations.

This study aims to explore the possibility of using a multilayer artificial neural network for the classification between children with autism spectrum disorder (ASD) and typically developing (TD) children based on short-time spontaneous hemodynamic fluctuations. Spontaneous hemodynamic fluctuations were collected by a functional near-infrared spectroscopy setup from bilateral inferior frontal gyrus and temporal cortex in 25 children with ASD and 22 TD children. To perform feature extraction and classification, a multilayer neural network called CGRNN was used which combined a convolution neural network (CNN) and a gate recurrent unit (GRU), since CGRNN has a strong ability in finding characteristic features and acquiring intrinsic relationship in time series. For the training and predicting, short-time (7 s) time-series raw functional near-infrared spectroscopy (fNIRS) signals were used as the input of the network. To avoid the over-fitting problem and effectively extract useful differentiation features from a sample with a very limited size (e.g., 25 ASDs and 22 TDs), a sliding window approach was utilized in which the initially recorded long-time (e.g., 480 s) time-series was divided into many partially overlapped short-time (7 s) sequences. By using this combined deep-learning network, a high accurate classification between ASD and TD could be achieved even with a single optical channel, e.g., 92.2% accuracy, 85.0% sensitivity, and 99.4% specificity. This result implies that the multilayer neural network CGRNN can identify characteristic features associated with ASD even in a short-time spontaneous hemodynamic fluctuation from a single optical channel, and second, the CGRNN can provide highly accurate prediction in ASD.

Recognition of a Virtual Maze Scene Using Simulated Prosthetic Vision.

A real-time psychological physics experimental platform was built to aid in discovering the best real-time image processing strategy for the limited number of electrodes, and in providing the most useful prosthetic implant visual information. A number of maze pathfinder tasks were performed at low resolution to simulate use by blind individuals in performing daily visual activities. In this study, simple (5 rows × 5 columns), medium (8 × 8), and complex (11 × 11) maze models were created in 3DMAX. The models were constructed by using Unity to build virtual scenes for real-time pixel processing, including binarization, color inversion, and matching to templates with three different resolutions (16 × 16, 24 × 24, 32 × 32). Subjects completed maze pathfinding tasks using 45° and 60° views of the labyrinth at the 32 × 32 resolution to determine the optimal viewing angle. The time required to find the maze entrance and complete the maze were analyzed along with the rate of maze completion (accuracy) at various resolutions. In the first experiment, the average time required to find the entrance and the average maze pathfinding duration were significantly longer at 60°. Therefore, the 45° view provided the best perspective. In the second experiment, the angle was fixed to 45°. As the maze difficulty increased, the average time needed to find the maze entrance decreased, but the average time required to complete the maze increased. When the difficulty of the maze was fixed, the time required to find the maze entrance and solve the maze decreased when the resolution increased. The accuracy with which the maze path was identified increased as well. The average maze pathfinding time at 24 × 24 was significantly less than at 16 × 16. A similar trend was observed when the average maze pathfinding times at 32 × 32 and 24 × 24 were compared. At 32 × 32, the average pathfinding accuracy was 100%. This indicates that 32 × 32 is an effective resolution for maze pathfinding.

Recognition of a Virtual Scene via Simulated Prosthetic Vision.

In order to effectively aid the blind with optimal low-resolution vision and visual recovery training, pathfinding and recognition tests were performed using a simulated visual prosthetic scene. Simple and complex virtual scenes were built using 3DMAX and Unity, and pixelated to three different resolutions (32 × 32, 64 × 64, and 128 × 128) for real-time pixel processing. Twenty subjects were recruited to complete the pathfinding and object recognition tasks within the scene. The recognition accuracy and time required were recorded and analyzed after the trials. In the simple simulated prosthetic vision (SPV) scene, when the resolution was increased from 32 × 32 to 48 × 48, the object recognition time decreased from 92.19 ± 6.97 to 43.05 ± 6.08 s, and the recognition accuracy increased from 51.22 ± 8.53 to 85.52 ± 4.93%. Furthermore, the number of collisions decreased from 10.00 ± 2.31 to 3.00 ± 0.68. When the resolution was increased from 48 × 48 to 64 × 64, the object recognition time further decreased from 43.05 ± 6.08 to 19.46 ± 3.71 s, the recognition accuracy increased from 85.52 ± 4.93 to 96.89 ± 2.06%, and the number of collisions decreased from 3.00 ± 0.68 to 1.00 ± 0.29. In complex scenes, the time required to recognize the room type decreased from 115.00 ± 23.02 to 68.25 ± 17.23 s, and object recognition accuracy increased from 65.69 ± 9.61 to 80.42 ± 7.70% when the resolution increased from 48 × 48 to 64 × 64. When the resolution increased from 64 × 64 to 128 × 128, the time required to recognize the room type decreased from 68.25 ± 17.23 to 44.88 ± 9.94 s, and object recognition accuracy increased from 80.42 ± 7.71 to 85.69 ± 7.39%. Therefore, one can conclude that there are correlations between pathfinding and recognition. When the resolution increased, the time required for recognition decreased, the recognition accuracy increased, and the number of collisions decreased. Although the subjects could partially complete the recognition task at a resolution of 32 × 32, the recognition time was too long and recognition accuracy was not good enough to identify simple scenes. Complex scenes required a resolution of at least 48 × 48 for complete recognition. In addition, increasing the resolution shortened the time required to identify the type of room, and improved the recognition accuracy.