: Applicants' experiences with discrimination explain their reactions to algorithms in personnel selection

Algorithms might prevent prejudices and increase objectivity in personnel selection decisions, but they have also been accused of being biased. We question whether algorithm-based decision-making or providing justifying information about the decision-maker (here: to prevent biases and prejudices and to make more objective decisions) helps organizations to attract a diverse workforce. In two experimental studies in which participants go through a digital interview, we find support for the overall negative effects of algorithms on fairness perceptions and organizational attractiveness. However, applicants with discrimination experiences tend to view algorithm-based decisions more positively than applicants without such experiences. We do not find evidence that providing justifying information affects applicants—regardless of whether they have experienced discrimination or not.     

Should I invite them? Bystanders' inclusivity judgements towards outgroup victims and ingroup bullies in intergroup bullying

This study examined bystanders' judgements of bullying and their inclusivity towards refugee victims and ingroup bully peers. Participants included 587 Turkish adolescents (M_age = 13.14, SD = 1.60) who were presented with two stories: intergroup (Syrian refugee victim) and intragroup (Turkish victim) bullying. They indicated acceptability of bullying, retaliation, and how likely they would be to include victims and bullies in different social contexts. Empathy, prejudice, desired social distance, and peer norms towards Syrian refugees were examined as predictors. Adolescents in schools with a higher number of Syrian peers were more likely to expect they would include the Syrian victim than adolescents in schools with a lower number of Syrian peers. Further, adolescents with higher empathy were more likely to include the Syrian victim while adolescents with higher prejudice and desired social distance were less likely to include the Syrian victim. The results highlight the importance of attending to bystanders' future interactions with victims and bullies, as bystanders have the opportunity to challenge injustice by promoting inclusive school climates in diverse societies. Please refer to the Supplementary Material section to find this article's Community and Social Impact Statement .     

The impact of customer engagement on customer happiness

Previous studies have found relationships between variables that predict happiness and engagement. While this suggests that engagement is a basis for generating customer happiness, the literature does not provide sufficient confirmatory evidence. Thus, the effects of different engagement intensities remain unclear. Focusing on conditions related to the Starbucks brand, this study analyzed how (1) passive and active engagement impacted customer happiness and (2) happiness impacted word of mouth (WOM) and purchase intention. Data were collected through an online survey among 802 Peruvian customers, all of whom were recruited via non-probabilistic sampling. Because two exogenous variables were considered formative (i.e., customer engagement and customer happiness), a partial least-squares structural equation model (PLS-SEM) was used for the analysis. In sum, engagement (both passive and active) significantly and positively impacted customer happiness, which then positively impacted WOM and purchase intention. Given that engagement is associated with variables that are broadly related to happiness, this study makes new contributions by (i) clarifying how customer engagement can generate customer happiness, (ii) identifying differences between active and passive engagement, and (iii) adding evidence for use in the debate on the purpose of marketing and traditional practices such as engagement. As discussed in the paper, these findings have theoretical and practical implications for brand managers.     

One-year prediction of cognitive decline following cognitive-stimulation from real-world data

Clinical evidence based on real-world data (RWD) is accumulating exponentially providing larger sample sizes available, which demand novel methods to deal with the enhanced heterogeneity of the data. Here, we used RWD to assess the prediction of cognitive decline in a large heterogeneous sample of participants being enrolled with cognitive stimulation, a phenomenon that is of great interest to clinicians but that is riddled with difficulties and limitations. More precisely, from a multitude of neuropsychological Training Materials (TMs), we asked whether was possible to accurately predict an individual's cognitive decline one year after being tested. In particular, we performed longitudinal modelling of the scores obtained from 215 different tests, grouped into 29 cognitive domains, a total of 124,610 instances from 7902 participants (40% male, 46% female, 14% not indicated), each performing an average of 16 tests. Employing a machine learning approach based on ROC analysis and cross-validation techniques to overcome overfitting, we show that different TMs belonging to several cognitive domains can accurately predict cognitive decline, while other domains perform poorly, suggesting that the ability to predict decline one year later is not specific to any particular domain, but is rather widely distributed across domains. Moreover, when addressing the same problem between individuals with a common diagnosed label, we found that some domains had more accurate classification for conditions such as Parkinson's disease and Down syndrome, whereas they are less accurate for Alzheimer's disease or multiple sclerosis. Future research should combine similar approaches to ours with standard neuropsychological measurements to enhance interpretability and the possibility of generalizing across different cohorts.     

“Keep them out to save our inside:” discourses on immigration by the Spanish far right

Vox is a far-right, Spanish political party that has steadily grown to become the third main party in the national congress. Immigration is a major presence in Vox's political agenda. Through Critical Discourse Analysis, we analyze the party's public speeches and Twitter communications on immigration in the last 3 years, from the beginning of the COVID-19 pandemic in 2020 to the Ukraine-Russia war in 2022. These contexts have provided a fertile ground for Vox's concerns with the protection of national borders, the criminalization of African and irregular immigrants, and the Spanish Government's ineffectiveness to protect the Spaniards' homes. Vox's main discursive strategies entail constructions of migrants and migration based on dichotomous binaries, culture clash, exclusionary discourses of domopolitics, and fears of imminent social and cultural changes. These constructions are based on the unproblematized belief on essential and unchangeable values that forge the identity of the homeland, which is implicitly threatened by immigrants. Against the migratory invasion, Vox constitutes itself as the ethical protector of the Spanish society and nation, “out of care for the insiders and not out of hatred for outsiders.”     

Impact of artificial fissioning and social networks on levels of aggression and affiliation in primates

Levels of aggression may be affected by stability of social relationships or by population density. A number of studies of nonhuman primates have indicated that spatial density influences agonistic activity levels less than does social density. Artificial fissioning of a captive troop of rhesus macaques was undertaken and the resultant differences in patterns of aggression and affiliation were examined. If population density has a major effect on levels of aggression, then fissioning will result in a decrease in aggression; if social stability has a major effect on levels of aggression, then fissioning is likely to be accompanied by an increase in aggression. An increase in rates of both aggressive and affiliative behavior resulted from artificial troop fissioning. These findings concur with other studies that have concluded that social stability is a more important determinant of primate aggression than is population density. Nonhuman primates use affiliative mechanisms to adjust their behavior when population density increases such that potentially adverse consequences of crowding are avoided.