Der Einsatz von künstlicher Intelligenz im HR: Die Wirkung und Förderung der Akzeptanz von KI-basierten Recruiting-Tools bei potenziellen Nutzern

Unrealistic Optimism Regarding Artificial Intelligence Opportunities in Human Resource Management

“…Followup studies could interview individuals at several points to uncover temporal changes in the AI in HRM perception in general and unrealistic optimism in particular. Including other languages and participants from other states could add further robustness to the presented results or shed light on cultural differences (Dahm & Dregger, 2019;Vrontis et al, 2022). Additionally, as several questions aimed at other companies in general, future research could distinguish between companies of similar industry and size and those with different attributes.…”

“…Focusing on HRM, possible extensions of this study may include switching the perspective from company intern to company extern. Like Dahm & Dregger (2019), one may focus on the applicants' perspective when surveying (Vrontis et al, 2022). In general, as Vrontis et al (2022) note, research on AI in HRM is scarce and needs further examination.…”

“…Résumés may also be sourced via the internet (Channabasamma et al, 2021). Automated processing outperforms manual screening in both speed and precision (Dahm & Dregger, 2019;. 6.…”

Highly Accurate, But Still Discriminatory

“…During the asynchronous video interview, candidates must record their answers to certain questions and upload them to a platform. Facial expressions (e.g., smiles, head gestures, facial expression), language (e.g., word counts, topic modeling, complexity, variety), and prosodic information (e.g., pitch, intonation, and pauses) are extracted by an algorithm, resulting in a personality profile of the applicant (Dahm and Dregger 2019;Naim et al 2016). Previous studies have shown that faces and speech are rich sources of cues for predicting personality (Biel et al 2012).…”

“…One often-used application of HR analytics in the recruiting context is algorithmic video analysis, where firms receive an evaluation of each applicant and a prediction of the applicants' job performance. The algorithmic video analysis takes place asynchronously; the applicants record a video of themselves, which is then algorithmically evaluated (Langer et al 2019;Dahm and Dregger 2019). Limited time and resources of recruiters simultaneously managing large pools of applicants are some of the main reasons for the rapid growth of algorithmic decision making in many companies (Leicht-Deobald et al 2019).…”