One of the most prominent issues in psychopathy is the inability to adequately monitor one’s performance and learn from one’s mistakes. We investigated the relationship between psychopathic traits, as measured with the Youth Psychopathy Inventory – Short Version, and both early and late error-related brain activity in an at-risk sample of male young adults. These multi-problem young adults (age 18–27) are severely dysfunctional in society and suffer from multiple problems including financial problems, delinquency, psychological problems, and drug use. Our final sample consisted of 115 multi-problem young adults and 26 controls. Participants performed an Eriksen-Flanker task during EEG measurements. We used the difference wave of the error-related negativity (ΔERN) as a measure of early error processing and the error positivity (Pe) as a measure of late error processing. Multi-problem young adults showed reduced ERN amplitudes compared to controls, but did not differ in Pe amplitude. We found no statistically significant relation between psychopathic traits and ERN and Pe amplitudes within the multi-problem group. Thus, we found evidence for dysfunctional error-processing in multi-problem young adults compared to controls. However, within the multi-problem sample we did not find evidence for a relationship between psychopathic traits and dysfunctional error-processing. One explanation may be that this is due to the specific developmental stage of our young adult participants in which a transition between error-processing deficits, as present in adolescents high in psychopathic traits, and error-processing overcompensation, as present in adults high in psychopathic traits, may occur.
Keywords: Psychopathy | Electroencephalography | Event-related potential | Young adulthood |Error-processing | Error-related negativity

This article aims to investigate how service providers are employing their channels to support the handling of customer complaints in an online to offline era. It provides a timely contribution by characterizing multichannel recovery practices, discussing its implications for customers, and discovering new trends. The study employs a qualitative multi-method research, which includes not only more than one method of collecting data, but also more than one method of analyzing data. Data collection involved 50 records of customer complaints, 10 semistructured interviews, direct observation and internal bank reports. The results suggest that multichannel customers are not willing to interact with a large number of channels to solve their problems leading to a high number of interactions. Customers expect a complex recovery not in terms of interactivity but in terms of depth. Recovery solutions, such as apologizing and monetary compensations are non-permanent solutions, are inefficient in the long term and imply financial losses. Despite the investment that is required, this investigation advocates for permanent solutions. To avoid service failures and complex recovery processes, it is possible that companies are improving their operations management in search of new strategies that are blurring the boundaries of O2O into a mix of offline and online channels (O2).
Keywords: Multichannel services | Failure | Recovery | Operations management | Offline channels | Online channels

Although machine learning methods have been used as an outcome prediction tool in many fields, their utilization in predicting incident outcome in occupational safety is relatively new. This study tests the performance of machine learning techniques in modeling and predicting occupational incidents severity with respect to accessible information of injured workers in agribusiness industries using workers’ compensation claims. More than 33,000 incidents within agribusiness industries in the Midwest of the United States for 2008–2016 were analyzed. The total cost of incidents was extracted and classified from workers’ compensation claims. Supervised machine learning algorithms for classification (support vector machines with linear, quadratic, and RBF kernels, Boosted Trees, and Naïve Bayes) were applied. The models can predict injury severity classification based on injured body part, body group, nature of injury, nature group, cause of injury, cause group, and age and tenure of injured workers with the accuracy rate of 92–98%. The results emphasize the significance of quantitative analysis of empirical injury data in safety science, and contribute to enhanced understanding of injury patterns using predictive modeling along with safety experts’ perspectives with regulatory or managerial viewpoints. The predictive models obtained from this study can be used to augment the experience of safety professionals in agribusiness industries to improve safety intervention efforts.
Keywords: Injury severity classification | Injury severity prediction | Machine learning

This paper investigates the causal effects of inside debt on banks risk-taking behavior by using a quasi-natural experiment of compensation deferring policy in the Chinese banking industry. We find that the policy reduces banks risk-taking by approximately 16.25%. Banks with high levels of government control significantly reduced their risk-taking after the compensation deferring policy was enacted, while those with low levels did not have the same response. By showing that CEOs compensation deferring significantly reduces banks risk-taking in an emerging market, we offer direct evidence for the academic understanding of the governance role of inside debt in emerging markets with weak country-level investor protection. Our results provide timely empirical evidence for government regulators who are concerned about the costs and benefits of banks risk shifting or the risk of the financial system.
Keywords: Deferred Compensation | Inside Debt | Banking Industry | Risk-shifting | China