International Journal of Selection and Assessment最新文献

While investments in employer branding are known for improving initial applicant attraction, their consequences reach far beyond. Building upon applicant attribution-reaction theory (AART), we explore the impact of employer brand equity on applicant reactions following a rejection. In particular, this study adopts a three-wave quasi-experimental vignette design to examine how different levels of employer brand equity (high vs. low) influence changes in organizational attractiveness after receiving a rejection letter (prototypical vs. appreciative). Data were collected from 236 Dutch and Belgian respondents who were (soon to be) active in the labor market, either being (self-)employed or final-year students. Results from a repeated-measures ANOVA reveal that an appreciative rejection letter may fully mitigate the negative effects of rejection for organizations with high employer brand equity. Conversely, prototypical rejection letters significantly decrease organizational attractiveness in high-equity organizations, undermining previously built employer brand equity. These findings underscore the importance of living the employer brand throughout the candidate journey and provide actionable insights for research and practice.

Despite frequent use of the term “candidate experience” in practice, academic research on the topic remains limited. This study examines what candidates perceive as memorable during recruitment and selection, as these experiences are more likely to shape lasting impressions of the hiring organization. Using the critical incident technique, we analyzed 278 memorable experiences from 202 candidates across three organizations. A thematic analysis revealed two key factors shaping these experiences: (1) eight categories of experience triggers embedded in social, structural, and physical recruitment contexts, and (2) three perceptions activated through sensemaking (value, fairness, and person-environment fit). Drawing on these findings, we propose a conceptual framework that describes how various memorable candidate experiences arise. The study offers implications for both theory and recruitment practice, including guidance for designing candidate-centered processes and a research agenda for advancing candidate experience scholarship.

SJTs have traditionally been conceptualized as low-fidelity simulations of how respond to work-related situations. Yet, several studies demonstrated that situation descriptions were necessary to solve some SJT items, whereas they were not needed for other SJT items. So far, no solid support was found for various factors (e.g., item characteristics, presentation format, instructions, and content domain) that make some situation descriptions relevant and others irrelevant for responding to SJT items. Building on trait activation theory, we posit that trait-relevant situational cues serve as an ignored factor in SJT situation descriptions. Across two main studies (N₁ = 269, N₂ = 1,092), we manipulated the availability of trait-relevant situational cues in SJT items to examine their impact on SJT scores and convergent validity. We found a main effect for the availability of trait-relevant situational cues on the proportion of correctly solved SJT items, but only a marginal impact on convergent validity. The current study thus helps explaining why some SJT items can be situated on the context-dependent side, whereas other items are on the context-independent side. At a practical level, this suggests that more systematically embedding trait-relevant situational cues into SJT descriptions may be a helpful design consideration to make SJT items more context-dependent, even if its impact on convergent validity appears to be limited.

It is well established that combining information via a simple algorithm (mechanical combination) results in better hiring decisions than a holistic combination. Nevertheless, mechanical combination is perceived negatively and rarely used in practice. Transparency is an often-mentioned determinant of algorithmic trust in the AI-literature, but has hardly received attention regarding mechanical judgment. Therefore, in four studies (total N = 1205), we experimentally investigated the effect of various operationalizations of transparency on the perceptions of simple, mechanical algorithmic hiring procedures among decision-makers and applicants. Quantitative analyses were supplemented with thematic analyses of open-ended responses. The original between-subjects studies hardly showed differences between conditions, but open question responses indicated that this may have been due to an emphasis on “filler information.” Subsequent within-subjects studies did show the expected positive effects of transparency, but mostly with small effect sizes. However, both applicants and decision-makers indicated a strong preference for transparent algorithms over opaque algorithms and over holistic judgment when asked to choose directly. Moreover, unexpectedly, compared to holistic judgment, the applicant sample showed more positive perceptions, even for an opaque algorithm. Open question responses suggest that both groups generally perceived algorithms as fairer, but not more valid than holistic judgment, but the quantitative results did not fully support that pattern. In addition, the open responses suggest that while transparency is requested when not provided, it may also evoke more scrutiny when it is, which may not necessarily affect perceptions positively.

Interview anxiety is a common experience. It can negatively impact interview performance and reduce the predictive validity of the interview. This article describes the development of a short measure of state interview anxiety, the SIA-6. Across two studies (N = 450 and N = 235) we describe the process of generating and selecting items and provide evidence of reliability and construct validity. The new measure correlates r = 0.66 with an established multi-dimensional measure of trait interview anxiety, suggesting it is related, but not redundant with the existing measure. The SIA-6 fits the nomological network of interview anxiety expected based on past research and will be a useful tool for future research on state interview anxiety.

The use of video interviews in personnel selection is increasing, yet less favorable applicant reactions to these interviews remain a concern among organizations. Alongside replicating prior findings, this study examines whether explaining the environmental benefits of video interviews can improve applicant perceptions. We investigate differences between synchronous (SVIs) and asynchronous video interviews (AVIs), focusing on fairness perceptions, organizational attractiveness, and interview motivation. Findings replicate that SVIs are generally perceived more favorably than AVIs, while explanations of environmental benefits can improve certain fairness perceptions and perceived organizational attractiveness. Additionally, pro-environmental attitudes do not significantly moderate the effect of explanations. These results highlight the potential of environmental explanations in recruitment regardless of applicants' pro-environmental attitudes.

The demand for telework possibilities among applicants is often driven by perceived productivity gains. This raises the question of whether expressing this preference affects hiring intentions. A preference for telework, on the one hand, and justifying it through productivity, on the other, may send conflicting signals to recruiters regarding notions of the ideal worker. In this paper we therefore investigate how expressing a desire for telework motivated by productivity reasons impacts the probability of receiving an interview and what it signals to recruiters. To this end, we set up a vignette experiment in which recruiters evaluate fictitious applicants for different jobs. As a result of this experimental set-up, the answers to our research questions can be interpreted causally, with external validity supported by the heterogeneity of the jobs included in the experiment. Our results indicate that expressing a productivity-motivated telework preference might negatively impact interview likelihood, although this effect is context-dependent and shaped by recruiter, job, and applicant characteristics.

Game-based assessments (GBAs) have become increasingly popular in personnel selection. However, research on perceptions of GBAs has yielded mixed results, highlighting the need to explore variables that may shape perceptions of GBAs. Therefore, we examined whether test takers' occupational background and other person-related characteristics (i.e., gender, age, video game usage, openness to experience) are associated with their perceptions of GBAs. N = 179 individuals from technical and social occupational backgrounds rated their perceptions of GBAs and non-gamified tests. We found that GBAs were generally rated more positively than non-gamified tests in terms of organizational attractiveness, behavioral intentions toward the organization, enjoyment, and modernity. Furthermore, test takers' occupational background and several individual difference variables were related to perceptions of GBAs. These findings show the need to include individual difference variables as antecedents in applicant reaction models.

The adoption of artificial intelligence (AI) decision-making in the workplace poses a moral issue beyond mere technology acceptance, considering the potential consequences of algorithmic management to individuals' professional well-being. In view of its pluralistic approach to human morality, we adopt Moral Foundations Theory (MFT) as our theoretical lens through which to study reactions to AI-led decision-making in selection. Using a qualitative approach, we explore individuals' reactions to the idea of AI-evaluated interviews, mapping these reactions onto moral foundations, identifying novel sub-themes specific to the context of AI in selection. Using 33 interviews with working adults, we find that all six moral foundations - care, fairness, authority, loyalty, sanctity, and liberty - are evoked when discussing the implementation of AI in selection. We discuss how these moral foundations manifest themselves in this AI decision making context, and articulate theoretical and practical implications.