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The rapid development of the Internet in China has profoundly affected the country's charities, which many people support through online donations (e.g. providing financial help) and charity information forwarding (a new behavior of participating in online charities via social media). However, the development of online charities has been accompanied by many problems, such as donation fraud and fake charity information, which adversely affect social kindness. The purpose of this paper is to understand people's online donation and forwarding behaviors and to explore the mechanisms of such behaviors from the perspectives of cognitive-based trust and emotional-based empathic concern.

This study developed a research model based on the elaboration likelihood model (ELM) and stimulus–organism–response (SOR) model. The researchers obtained 287 valid samples via a scenario-based experimental survey and conducted partial least squares structural equation modeling (PLS-SEM) to test the model.

The results indicated that (1) online donation intention is motivated by rational-based trust and emotional-based empathic concern; (2) online charity information forwarding is triggered only when trust is built, and there is no significant correlation between empathic concern and forwarding intention; and (3) content quality, initiator credibility, and platform reputation are three critical paths to promote trust; in addition, an individual's empathic concern can be motivated by the emotional appeal.

This study highlights the different mechanisms of donation and forwarding behaviors and provided theoretical measures for motiving trust and empathic concern in the online context to promote people's participation in online charity.

Using the sample between 2005 and 2011, the purpose of this paper is to investigate the effect of a new fund entry on incumbents using the overlap measure in portfolio holdings.

Empirical methodology is used in this study.

The authors find that incumbents that have a higher overlap with the entrants underperform subsequently. Based on the characteristic-based approach of Daniel et al. (1997), the authors find that the characteristic selectivity component is negatively correlated with the overlap measure, and thereby the decline in performance is driven by the stock-picking ability. The authors also discuss the unobserved actions of incumbents using the approach proposed by Kacperczyk et al. (2008) and find that incumbent unobserved actions do not benefit mutual fund investors in China. Finally the authors find that investors respond to the supply-side competition between entrants and incumbents quickly. These findings help us understand the mutual fund completion in China.

The findings in this study can help scholars, industry experts and regulatory authorities to understand the effect of competition in Chinese mutual fund industry.

Based on the theory of limited attention, the purpose of this paper is to investigate whether the investor behavior is influenced by attention, using the sample from earning announcement in China.

Empirical research using the earning announcement data in China. Specifically, the authors use the sample from 2005 to 2010 in listed A-share firms with earning announcements in Shanghai and Shenzhen stock market. Panel data regressions are used with Newey and West (1987) to correct for the potential heteroskedasticity and autocorrelation. The empirical results strongly support the hypothesis that limited attention impact investor behavior in China.

The authors find that the immediate price and volume reaction to earning surprise is much weaker and post-announcement drift is much stronger when a greater number of firms make earning announcements on the same day. The authors explain these findings mainly from behavioral bias. When investors process multiple information signals immediately or perform multiple objects simultaneously, their attention will be allocated selectively due to cognitive constraints. Such limited attention causes severe underreaction to immediate earnings announcement, therefore leads to mispricing abnormal related to public accounting information. In the long-run, the market adjusted and there is post-announcement drift.

Consistent with Hirshleifer et al. (2009), the findings in this study indicate that individual investors’ behaviors are influenced by their limited attention in China. The results are different from Yu and Wang (2010) conclusions that same-day concentrated announcement help investors and facilitate information dissemination in China. The findings are explained by the investor distraction hypothesis proposed by Hirshleifer et al. (2009) that investor distraction causes market underreaction.

The arrival of simultaneously extraneous earning information cause market prices and trading volume to react slowly to the relevant news about a firm because competing information signals distract investor from a given firm, causing market price to underreact to relevant news. These finding help us understand investor behavior and the impact of limited attention on security market.

Investor limited attention not only affects their stock-buying behavior, but also has an important impact on the efficiency of security market. Specifically, limited attention drive immediate underreaction to earning announcement and the post-earning announcement drift, especially when a greater number of same-day earning announcements are made by other firms.

Limited attention affects security market in China.

This study aims to propose an enhanced eco-driving strategy based on reinforcement learning (RL) to alleviate the mileage anxiety of electric vehicles (EVs) in the connected environment.

In this paper, an enhanced eco-driving control strategy based on an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed for connected EVs. The EEDC-HRL simultaneously controls longitudinal velocity and lateral lane-changing maneuvers to achieve more potential eco-driving. Moreover, this study redesigns an all-purpose and efficient-training reward function with the aim to achieve energy-saving on the premise of ensuring other driving performance.

To illustrate the performance for the EEDC-HRL, the controlled EV was trained and tested in various traffic flow states. The experimental results demonstrate that the proposed technique can effectively improve energy efficiency, without sacrificing travel efficiency, comfort, safety and lane-changing performance in different traffic flow states.

In light of the aforementioned discussion, the contributions of this paper are two-fold. An enhanced eco-driving strategy based an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed to jointly optimize longitudinal velocity and lateral lane-changing for connected EVs. A full-scale reward function consisting of multiple sub-rewards with a safety control constraint is redesigned to achieve eco-driving while ensuring other driving performance.

Driven by the dual-carbon target and the widespread digital transformation, leveraging digital technology (DT) to facilitate sustainable, green and high-quality development in heavy-polluting industries has emerged as a pivotal and timely research focus. However, existing studies diverge in their perspectives on whether DT’s impact on green innovation is synergistic or leads to a crowding-out effect. In pursuit of optimizing the synergy between DT and green innovation, this paper aims to investigate the mechanisms that can be harnessed to render DT a more constructive force in advancing green innovation.

Drawing from the theoretical framework of resource orchestration, the authors offer a comprehensive elucidation of how DT intricately influences the green innovation efficiency of enterprises. Given the intricate interplay within the synergistic relationship between DT and green innovation, the authors use the fuzzy-set qualitative comparative analysis method to explore diverse configurations of antecedent conditions leading to optimal solutions. This approach transcends conventional linear thinking to provide a more nuanced understanding of the complex dynamics involved.

The findings reveal that antecedent configurations fostering high green innovation efficiency actually differ across various stages. First, there are three distinct configuration patterns that can enhance the green technology research and development (R&D) efficiency of enterprises, namely, digitally driven resource integration (RI), digitally driven resource synergy (RSy) and high resource orchestration capability. Then, the authors also identify three configuration patterns that can bolster the high green achievement transfer efficiency of enterprises, including a digitally optimized resource portfolio, digitally driven RSy and efficient RI. The findings not only contribute to advancing the resource orchestration theory in the digital ecosystem but also provide empirical evidence and practical insights to support the sustainable development of green innovation.

The findings can offer valuable insights for enterprise managers, providing decision-making guidance on effectively harnessing the innovation-driven value of internal and external resources through resource restructuring, bundling and leveraging, whether with or without the support of DT.

The research findings contribute to heavy-polluting enterprises addressing the paradoxical tensions between digital transformation and resource constraints under environmental regulatory pressures. It aims to facilitate the simultaneous achievement of environmental and commercial success by enhancing their green innovation capabilities, ultimately leading to sustainability across profit and the environment.

Compared with previous literature, this research introduces a distinctive theoretical perspective, the resource orchestration view, to shed light on the paradoxical relationship on resource-occupancy between DT application and green innovation. It unveils the “black box” of how digitalization impacts green innovation efficiency from a more dynamic resource-based perspective. While most studies regard green innovation activities as a whole, this study delves into the impact of digitalization on green innovation within the distinct realms of green technology R&D and green achievement transfer, taking into account a two-stage value chain perspective. Finally, in contrast to previous literature that predominantly analyzes influence mechanisms through linear impact, the authors use configuration analysis to intricately unravel the complex influences arising from various combinatorial relationships of digitalization and resource orchestration behaviors on green innovation efficiency.