Utilizing role theory, we investigate the potential negative relationship between employees' moral ownership and their creativity, and the mitigating effect of ethical leadership in this relationship. We argue that employees higher on moral ownership are likely to take more moral role responsibility to ensure the ethical nature of their own actions and their environment, inadvertently resulting in them being less able to think outside of the box and to be creative at work. However, we propose that ethical leaders can relieve these employees from such moral agent role, allowing them to be creative while staying moral. We adopt a multimethod approach and test our predictions in 2 field studies (1 dyadic-based from the United States and 1 team-based from China) and 2 experimental studies (1 scenario-based and 1 team-based laboratory study). The results across these studies showed: (a) employee moral ownership is negatively related to employee creativity, and (b) ethical leadership moderates this relationship such that the negative association is mitigated when ethical leadership is high rather than low. Moreover, the team-based laboratory study demonstrated that moral responsibility relief mediated the buffering effect of ethical leadership. We discuss implications for role theory, ethicality, creativity, and leadership at work. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
In an increasingly competitive and performance-oriented society, workaholic leadership is becoming increasingly common and is even embraced and supported by many organizations. However, previous studies have not paid sufficient attention to the impact of workaholic leadership on employee psychology and behavior. This study, based on the conservation of resources (COR) theory, explores the effect of workaholic leadership on employee self-presentation. Through an empirical analysis of 256 employees' questionnaires, we found a significant positive impact between workaholic leaders and employee self-presentation. This process was achieved through the partly mediating mechanisms of employee workplace anxiety. Concurrently, segmentation supplies negatively moderated the relationship between workplace anxiety and self-presentation and the overall mediating mechanism. These findings provide important insights into the underlying mechanisms of workaholic leadership and employee behavior, which can be utilized to improve employee wellbeing and provide positive organizational outcomes.
This study investigates the impact of the COVID-19 pandemic on employee job performance trajectories, and further examines the moderating effects of different sources of status. Drawing from event system theory (EST), we propose that employee job performance decreases upon COVID-19 onset, but gradually increases during the postonset period. Furthermore, we argue that status from society, occupation, and workplace functions to moderate such performance trajectories. We test our hypotheses with a unique dataset of 708 employees that combines survey responses and job performance archival data over 21 consecutive months (10,808 observations) spanning the preonset, onset, and postonset periods of the initial encounter with COVID-19 in China. Utilizing discontinuous growth modeling (DGM), our findings indicate that the onset of COVID-19 created an immediate decrease in job performance, but such decrease was weakened by higher occupation and/or workplace status. However, the postonset period resulted in a positive employee job performance trajectory, which was strengthened for employees with lower occupational status. These findings enrich our understanding of COVID-19's impact on employee job performance trajectories, highlight the role of status in moderating such changes over time, and also provide practical implications to understand employee performance when facing such a crisis.
AbstractIn this paper, we show that the increasing popularity of machine learning improves market efficiency. By analysing the performance of a set of popular machine learning-based investment strategies, we find that profits from these strategies experience significant declines since the wide adoption of machine learning techniques, especially for profits based on the more preferred method of neural networks. These declines mainly come from long legs. Using the 'machine learning' Google search index as a proxy for machine learning-based trading intensity, we find that returns from the neural networks-based long–short and long-only strategies are weaker following high levels of machine learning intensity, while no relation is found between machine learning intensity and the short-only neural networks-based strategy.Keywords: Machine learningmarket efficiencymispricingneural networksarbitraging activities AcknowledgementsWe thank Shiyang Huang, Zhigang Qiu, Hanwen Sun, Chi-Yang Tsou, Yanyi Wang, Hong Xiang, Weinan Zheng, and participants at the 2021 International Forum on Development of FinTech for helpful comments and suggestions. Substantial parts of this research were done when Jian Feng was at Renmin University of China. All errors are our own.Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 For the survey results, see "The Machine Learning Trends Transforming Finance" published on 17 April 2019, https://www.refinitiv.com/perspectives/ai-digitalization/the-machine-learning-trends-transforming-finance/.2 Google search indices are available starting from 2004.3 For detailed reviews of this literature, see Welch and Goyal (Citation2008), Koijen and Van Nieuwerburgh (Citation2011), and Rapach and Zhou (Citation2013).4 Our sample starts in 1975 because we need sufficient quarterly accounting variables from Compustat to compute firm characteristics.5 These operations are typically referred to as activation functions. They play a key role in NN by adding non-linearity into the model while maintaining the feasibility of calculating gradients and optimisation. Commonly used non-linear operations are Sigmoid, Tanh, and ReLU, among others.
Abstract We draw on deonance theory and social learning theory to propose a framework that explains how individual team members with varying levels of leader–member exchange (LMX) with their team leader have different emotional and behavioral responses upon observing teammate‐directed abusive supervision. After employing a social relations paradigm with two‐wave round‐robin data collected from a sample of 378 engineers on 89 work teams, we did not find that witnessing teammate‐targeted abusive supervision increased sympathy for the targeted teammates, but we did find that observers with a higher level of LMX were more likely to legitimize such abuse and less likely to sympathize with its victims. Furthermore, we found that for individuals with a higher level of LMX, perceiving leaders' abusive supervision of teammates was negatively related to providing help to those teammates through the mediating role of sympathy for the teammates.
Geographic proximity is associated with significantly higher returns from short selling within London and the UK. Short trades by funds near the target headquarters are followed by larger negative abnormal returns. Proximity matters more for stocks that are smaller, more volatile, and less actively covered by sellside analysts, and less for large trades and trades following more proximate institutions' trades. Short trades are correlated geographically, with proximate funds more likely to short the same stocks. Geographically closer short trades predict more negative earnings surprises. Covering of short positions by more proximate institutions is followed by more positive abnormal stock returns.
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