This research investigates the link between rivalry and unethical behavior. We propose that people will be more likely to engage in unethical behavior when competing against their rivals than when competing against non-rival competitors. Across an archival study and a series of experiments, we found that rivalry was associated with increased unsportsmanlike behavior, use of deception, and willingness to employ unethical negotiation tactics. We also explored the psychological underpinnings of rivalry, which help to illuminate how rivalry differs from general competition and why it increases unethical behavior. The data reveal a serial mediation pathway whereby rivalry heightens the psychological stakes of competition (by increasing actors' contingency of self-worth and status concerns), which leads them to adopt a stronger performance approach orientation, which then increases unethical behavior. These findings highlight the importance of rivalry as a widespread, powerful, yet largely unstudied phenomenon with significant organizational implications. They also help to inform when and why unethical behavior occurs within organizations, and demonstrate that the effects of competition are dependent upon relationships and prior interactions between actors.

The Covid-19 pandemic has induced worldwide natural experiments on the effects of crowds. We exploit one of these experiments that took place over several countries in almost identical settings: professional football matches played behind closed doors within the 2019/20 league seasons. We find large and statistically significant effects on the number of yellow cards issued by referees. Without a crowd, fewer cards were awarded to the away teams, reducing home advantage. These results have implications for the influence of social pressure and crowds on the neutrality of decisions.

No abstract

An efficient market incorporates news into prices immediately and fully. Tests for efficiency in financial markets have been undermined by information leakage. We test for efficiency in sports betting markets – real‐world markets where news breaks remarkably cleanly. Applying a novel identification to high‐frequency data, we investigate the reaction of prices to goals scored on the ‘cusp’ of half‐time. This strategy allows us to separate the market's response to major news (a goal), from its reaction to the continual flow of minor game‐time news. On our evidence, prices update swiftly and fully.

This paper provides an overview of the R package gets, which contains facilities for automated general-to-specific (GETS) modeling of the mean and variance of a regression, and indicator saturation (IS) methods for the detection and modeling of outliers and structural breaks. The mean can be specified as an autoregressive model with covariates (an "AR-X" model), and the variance can be specified as an autoregressive log-variance model with covariates (a "log-ARCH-X" model). The covariates in the two specifications need not be the same, and the classical linear regression model is obtained as a special case when there is no dynamics, and when there are no covariates in the variance equation. The four main functions of the package are arx, getsm, getsv and isat. The first function estimates an AR-X model with log-ARCH-X errors. The second function undertakes GETS modeling of the mean specification of an 'arx' object. The third function undertakes GETS modeling of the log-variance specification of an 'arx' object. The fourth function undertakes GETS modeling of an indicator-saturated mean specification allowing for the detection of outliers and structural breaks. The usage of two convenience functions for export of results to EViews and Stata are illustrated, and L A T E X code of the estimation output can readily be generated.