Should I Trust the Artificial Intelligence to Recruit? Recruiters' Perceptions and Behavior When Faced With Algorithm-Based Recommendation Systems During Resume Screening

Resume screening assisted by decision support systems that incorporate artificial intelligence is currently undergoing a strong development in many organizations, raising technical, managerial, legal, and ethical issues. The purpose of the present paper is to better understand the reactions of recruiters when they are offered algorithm-based recommendations during resume screening. Two polarized attitudes have been identified in the literature on users' reactions to algorithm-based recommendations: algorithm aversion, which reflects a general distrust and preference for human recommendations; and automation bias, which corresponds to an overconfidence in the decisions or recommendations made by algorithmic decision support systems (ADSS). Drawing on results obtained in the field of automated decision support areas, we make the general hypothesis that recruiters trust human experts more than ADSS, because they distrust algorithms for subjective decisions such as recruitment. An experiment on resume screening was conducted on a sample of professionals (N = 694) involved in the screening of job applications. They were asked to study a job offer, then evaluate two fictitious resumes in a 2 × 2 factorial design with manipulation of the type of recommendation (no recommendation/algorithmic recommendation/human expert recommendation) and of the consistency of the recommendations (consistent vs. inconsistent recommendation). Our results support the general hypothesis of preference for human recommendations: recruiters exhibit a higher level of trust toward human expert recommendations compared with algorithmic recommendations. However, we also found that recommendation's consistence has a differential and unexpected impact on decisions: in the presence of an inconsistent algorithmic recommendation, recruiters favored the unsuitable over the suitable resume. Our results also show that specific personality traits (extraversion, neuroticism, and self-confidence) are associated with a differential use of algorithmic recommendations. Implications for research and HR policies are finally discussed.

Transformational Leadership, Ethical Leadership, and Participative Leadership in Predicting Counterproductive Work Behaviors: Evidence From Financial Technology Firms

Counterproductive work behaviors are a crucial issue for practice and academic because it influences employees’ job performance and career development. The present research conceptualizes Kahn’s employee engagement theory and employs transformational leadership, ethical leadership, and participative leadership as its antecedents to predict counterproductive work behaviors through a latent growth model. The present research collected empirical data of 505 employees of fintech businesses in Great China at three waves over 6 months. The findings revealed that as employees perceived higher transformational leadership, ethical leadership, and participative leadership at the first time point, they may demonstrate more positive growths in employee engagement development behavior, which in turn, caused more negative growths in counterproductive work behaviors. The present research stresses a dynamic model of the three leaderships that can alleviate counterproductive work behaviors through the mediating role of employee engagement over time.

Staying Quiet or Speaking Out: Does Peer Reporting Depend on the Type of Counterproductive Work Behavior Witnessed?

Abstract. Peer reporting, which occurs when employees report the occurrence of coworkers’ counterproductive work behavior (CWB) to organizational authorities, may help management detect CWBs that would otherwise be overlooked. We examined whether peer reporting depends on two CWB characteristics identified by Robinson and Bennett [(1995) A typology of deviant workplace behaviors: A multidimensional scaling study. Academy of Management Journal, 38, 555–572]: (a) the target of the CWB and (b) the severity of the CWB. Specifically, we hypothesized that witnesses are more likely to report CWBs targeting individuals than CWBs targeting the organization and that they are more likely to report serious CWBs than minor CWBs. Using a diversely employed sample ( N participants = 139; N observations = 3451), we found support for the hypothesized effects of CWB severity, but not for the hypothesized effects of CWB target.

On the effectiveness of peer reporting policies

Purpose

Unlike general codes of conduct, little is known about whether peer reporting policies achieve their intended purpose – that is, to increase the base rate of peer reporting counterproductive work behaviors (CWBs). The purpose of this paper is to use a person-situation perspective to examine if and when peer reporting policies impact the base rate of peer reporting CWBs.

Design/methodology/approach

The authors collected data from 271 employed participants and used moderated regression to examine whether policy presence and strength (situational variables) enhanced the relationship between a subjective obligation to report CWBs (person variable) and the base rate of peer reporting CWBs. This study also explored whether these interactions differ by CWB target (i.e. the organization vs coworkers).

Findings

Both situational variables – policy presence and policy strength – moderated the relationship between an obligation to report CWBs and the base rate of peer reporting CWBs. The interactions also differed by CWB target.

Originality/value

This study represents one of the initial academic investigations into the effectiveness of peer reporting policies. It primarily draws on the person-situation perspective to explain why peer reporting policies should influence the base rate of peer reporting CWBs. The results support the impact of peer reporting policies, but also suggest the benefit of examining different targets of CWB to help clarify when peer reporting policies are actually effective.