Search results

The rapid growth of professional esports has highlighted the lack of a universally recognised governing body to standardise operations and competition rules. This absence presents many challenges. A key concern is the well-being of professional esports players (e-pro-players), who often suffer from exhaustion. This study aims to examine the factors contributing to exhaustion among e-pro-players.

Using the conservation of resources theory, we developed a framework to explain the factors leading to e-pro-players’ exhaustion and the conditions under which it occurs. We tested this framework with 126 responses in a dyadic survey from e-pro-players and their coaches in China. Additionally, we gathered qualitative insights from 50 interviews with esports stakeholders to provide more context for our quantitative findings.

Our study found that e-pro-players’ intrinsic motivation to engage in training reduces their exhaustion, while their struggle to cope with uncertainty in esports environments (intolerance of uncertainty) increases it. The effect of intrinsic motivation is weaker for those who believe their talent for playing esports is fixed (entity belief) but stronger for those with high relational identification with their coaches. Additionally, the link between uncertainty intolerance and exhaustion is stronger in players with strong entity beliefs.

Our study sheds light on the factors contributing to e-pro-players’ exhaustion within the partially regulated professional esports environment, a phenomenon that significantly influences their overall well-being. Through the identification and examination of these factors and the conditions under which they affect exhaustion, we deepen the understanding of the drivers of exhaustion for e-pro-players who operate in an industry lacking standardised regulations.

The purpose of this study is to explore how the influence of the social network of the members of top management teams affects the firms’ innovation performance through organizational learning in cultural and creative industries in China.

Based on cultural and creative industries, this paper focuses on how the social network of members of top management teams affects innovation through organizational learning. Using upper Echelon theory and social capital theory, the paper puts forward the relationship between the top management team’s social network, organizational learning and innovation performance.

Drawing on the paradigm of organizational strategy duality (input-process-output), this paper constructs the conceptual model of “relational network – organizational learning − innovative performance” and attempts to reveal the relationship between the network, represented by the senior management network and organizational learning, and the mechanism behind their role in innovation performance. Finally, future research prospects are explored.

Based on the analysis of the internal mechanism between the top management team network, organizational learning and innovation performance, the influence mechanism framework for the cultural and creative industries’ executive team social network on enterprise innovation is finally obtained, which provides theoretical guidance and a practical operation path for enterprise management innovation.

This research makes a theoretical contribution to the duality of organizational strategy and provides a practical operation path for enterprises to build a social network, and thereby promote innovation capabilities.

This study aims to explore whether investors’ inattention is associated with firms’ environmental, social and governance (ESG) decoupling, which is defined as the misalignment between the implementation and incorporation of ESG policies.

Focusing on a sample of the components of ESG ratings for China Securities Index (CSI) 300 companies between 2017 and 2019, the authors test the relationship between firms’ ESG decoupling level and mutual fund investors’ distraction by applying exogenous shocks to their portfolios.

The results show that firms with distracted mutual fund investors engage in more external than internal ESG actions, leading to a high ESG decoupling level. Mutual fund investors use “threat of exit” rather than “voice” as a governance mechanism to influence corporate ESG decoupling. While external ESG actions mitigate stock price crash risk, internal ESG actions increase firm value; firms with a high ESG decoupling level suffer lower valuations.

This study has implications for increasing the congruence between firms’ external and internal ESG actions, thereby improving firms’ ESG performance and long-term economic outcomes.

This paper helps policy-makers and regulators to reassess how ESG policies can be implemented to be consistent with organizations’ core business activities.

Contributing to prior studies of greenwashing and corporate social responsibility decoupling, this paper extends decoupling literature by revisiting ESG impacts in an integrated framework and explores the antecedents of corporate ESG decoupling from the perspective of institutional investor monitoring.

Automatic defect detection is a fundamental and vital topic in the research field of industrial intelligence. In this work, the authors develop a more flexible deep learning method for the industrial defect detection.

The authors propose a unified framework for detecting defects in industrial products or planar surfaces based on an end-to-end learning strategy. A lightweight deep learning architecture for blade defect detection is specifically demonstrated. In addition, a blade defect data set is collected with the dual-arm image collection system.

Numerous experiments are conducted on the collected data set, and experimental results demonstrate that the proposed system can achieve satisfactory performance over other methods. Furthermore, the data equalization operation helps for a better defect detection result.

An end-to-end learning framework is established for defect detection. Although the adopted fully convolutional network has been extensively used for semantic segmentation in images, to the best knowledge of the authors, it has not been used for industrial defect detection. To remedy the difficulties of blade defect detection which has been analyzed above, the authors develop a new network architecture which integrates the residue learning to perform the efficient defect detection. A dual-arm data collection platform is constructed and extensive experimental validation are conducted.