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Abstract

Both older and younger job seekers face difficulties when entering the workforce. Qualification‐based targeted recruitment (QBTR) might be used to attract older/younger job seekers, yet how this strategy is perceived by older/younger job seekers has not been considered before. The present study fills this gap and investigated effects of negatively metastereotyped information in job ads (i.e., personality requirements or traits) on application intention and self‐efficacy of both older and younger job seekers. An experimental study ( N total = 556; 44.6% aged 50 or older, 55.4% aged 30 or younger) showed that negatively metastereotyped traits in job ads (e.g., “flexible”) lowered older job seekers’ application intention and that this effect was mediated by older job seekers’ self‐efficacy regarding that trait. No such effects were found among younger job seekers. Results showed that organizations can fail to attract older candidates because of the traits mentioned in job ads, which is particularly alarming when aiming to target age‐diverse applicants. Suggestions for practitioners and future research are formulated.

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... Thus, when job ads activate age-related metastereotypes that are negative in nature, this might pose a threat to older and younger job seekers' social age identity [19] and impact whether they intent to apply for the job [20]. Research indeed showed that negative metastereotypes in job ads lowered job attraction compared to job ads without negative metastereotypes for female job seekers [7] and ethnic minority job seekers [6]. ...
... Manipulations were situated in the profile section; profiles contained HEXACO-traits [62,63] that older job seekers held either negative or no negative metastereotypes about. These negatively metastereotyped and not negatively metastereotyped traits for older people were developed and pilot tested in a previous study of this research project [20]. A more detailed description of the procedure and results of this pilot study can be retrieved from the first author. ...
... Similar to Study 1, materials were fictional job advertisements but the manipulation of traits in the profile section was now tailored to younger job seekers: profiles contained traits that younger job seekers held either negative or no negative metastereotypes about. As in Study 1, we developed and pilot tested the traits in a previous study [20]. A more detailed description of the procedure and results of this pilot study can be retrieved from the first author. ...
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Building on social identity theory and cognitive models on information processing, the present paper considered whether and how stereotyped information in job ads impairs older/younger job seekers’ job attraction. Two eye-tracking experiments with older (Study 1) and younger job seekers (Study 2) investigated effects of negatively metastereotyped personality requirements (i.e., traits) on job attraction and whether attention to and memory for negative information mediated these effects. Within-participants analyses showed for both older and younger job seekers that job attraction was lower when ads included negative metastereotypes and that more attention was allocated towards these negative metastereotypes. Older, but not younger job seekers, also better recalled these negative metastereotypes compared to not negative metastereotypes. The effect of metastereotypes on job attraction was not mediated by attention or recall of information. Organizations should therefore avoid negative metastereotypes in job ads that may capture older/younger job seekers’ attention and lower job attraction.
... More research on the effects of multiple-group members is important as isolating one stereotyped characteristic (e.g., sex) while ignoring others (e.g., race or age) may oversimplify the reality of many job seekers who have multiple identities and therefore may face intersectional biases. The papers by Koçak et al. (2023) and Krings et al. (2022) ...
... Here is the result that ChatGPT produced (we only added the references to the respective papers): "The six abstracts all relate to personnel selection and industrial and organizational psychology. The first study examines the impact of negatively metastereotyped information in job ads on application intention and self-efficacy of both older and younger job seekers (Koçak et al., 2023). The second study examines the impact of self-promotion on interview performance ratings, finding that both older female and younger male candidates who engage in high self-promotion are regarded as less interpersonally warm and less likely to be hired (Krings et al., 2022). ...
... We now provide the summaries written by the editors of the Special Issue that are organized in line with the two common topics of the included papers: diversity and technology. Koçak et al. (2023) investigate the effects of negatively metastereotyped personality requirements in job advertisements on both younger and older job seekers' self-efficacy and application intentions. Metastereotypes refer to people's beliefs about stereotypes that out-group members hold about one's group (Vorauer et al., 1998). ...
... In all analyses, we controlled for whether women believed they possess the required competencies in the ads (Bandura, 2015;Tierney & Farmer, 2002;Van Hoye et al., 2015;Van Ryn & Vinokur, 1992). We measured participants' specific self-efficacy regarding the person requirements with one item for each person requirement, adapted from Koçak et al. (2023). An example item for the requirement 'being a leader' is: "I feel confident that I can be a good leader," measured on a 5-point Likert-type scale, with 1 = strongly disagree to 5 = strongly agree. ...
... However, given that people might also identify as non-binary, future research might consider the specific meta-stereotypes and recruitment or selection experiences of those individuals. Research has shown that meta-stereotypes also exist for other demographic groups such as older people, younger people, people with an ethnic minority background (Finkelstein et al., 2015;Koçak et al., 2023;Wille & Derous, 2017). One could therefore consider intersectionality and look at the specific negative versus positive meta-stereotypes that women of different ages and ethnic backgrounds hold, as well as the effect of those meta-stereotypes on recruitment or promotion outcomes. ...
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Women are still underrepresented at the top levels of organizations across Europe and the United States. Scholars have identified obstacles that hinder women’s climb to the top but have overlooked women’s perceptions of job advertisements for top-level positions as a potential barrier to top-level positions. The present study investigated the effects of meta-stereotyped person requirements (positive vs. negative) and their wording (dispositional vs. behavioral) in job ads for top-level executive positions on female candidates’ application intention, as well as the mediating effect of job attractiveness. An experimental field study in a large, Western European governmental organization (Nmain study = 432 female officers), preceded by a pilot study (verbal protocol analysis; Npilot = 19 female executives) showed that compared to positively meta-stereotyped person requirements, negatively meta-stereotyped person requirements reduced female candidates’ attraction to a job and, in turn, their intention to apply for top-level executive positions. The way person requirements were worded in job ads (i.e., in a behavioral versus dispositional way) also affected women’s perceived job attractiveness, yet this depended on the type of requirement. Implications are considered for drafting job ads to encourage more qualified female candidates to apply.
... Like other LLMs, ChatGPT might perpetuate and reinforce biases about specific demographic groups it has learned from its training data's patterns, language, and concepts, resulting in discriminatory responses to said CV screening task. Hate speech in online fora (Bliuc, Faulkner, Jakubowicz, & McGarty, 2018;Castaño-Pulgarín, Suárez-Betancur, Vega, & López, 2021;Ederer, Goldsmith-Pinkham, & Jensen, 2023) or harmful stereotypes about minority groups in pre-existing job advertisements (Koçak, Derous, Born, & Duyck, 2022;Wille & Derous, 2017), for example, can taint the models' training processes. The GPT-3 model instance has demonstrated anti-Muslim bias in word association tasks before, consistently linking Muslims with violence and terrorism (Abid, Farooqi, & Zou, 2021). ...
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Large language models offer significant potential for increasing labour productivity, such as streamlining personnel selection, but raise concerns about perpetuating systemic biases embedded into their pre-training data. This study explores the potential ethnic and gender bias of ChatGPT—a chatbot producing human-like responses to language tasks—in assessing job applicants. Using the correspondence audit approach from the social sciences, I simulated a CV screening task with 34,560 vacancy–CV combinations where the chatbot had to rate fictitious applicant profiles. Comparing ChatGPT’s ratings of Arab, Asian, Black American, Central African, Dutch, Eastern European, Hispanic, Turkish, and White American male and female applicants, I show that ethnic and gender identity influence the chatbot’s evaluations. Ethnic discrimination is more pronounced than gender discrimination and mainly occurs in jobs with favourable labour conditions or requiring greater language proficiency. In contrast, gender bias emerges in gender-atypical roles. These findings suggest that ChatGPT’s discriminatory output reflects a statistical mechanism echoing societal stereotypes. Policymakers and developers should address systemic bias in language model-driven applications to ensure equitable treatment across demographic groups. Practitioners should practice caution, given the adverse impact these tools can (re)produce, especially in selection decisions involving humans.
... Economists refer to this assessment based on group characteristics as statistical discrimination. Hate speech in online fora (Bliuc et al., 2018;Castaño-Pulgarín et al., 2021;Ederer et al., 2023) or harmful stereotypes about minority groups in pre-existing job advertisements (Koçak et al., 2022;Wille and Derous, 2017), for example, can taint the models' training processes. The GPT-3 model instance has demonstrated anti-Muslim bias in word association tasks before, consistently linking Muslims with violence and terrorism (Abid et al., 2021). ...
Preprint
Large language models offer significant potential for increasing labour productivity, such as streamlining personnel selection, but raise concerns about perpetuating systemic biases embedded into their pre-training data. This study explores the potential ethnic and gender bias of ChatGPT—a chatbot producing human-like responses to language tasks—in assessing job applicants. Using the correspondence audit approach from the social sciences, I simulated a CV screening task with 34,560 vacancy–CV combinations where the chatbot had to rate fictitious applicant profiles. Comparing ChatGPT’s ratings of Arab, Asian, Black American, Central African, Dutch, Eastern European, Hispanic, Turkish, and White American male and female applicants, I show that ethnic and gender identity influence the chatbot’s evaluations. Ethnic discrimination is more pronounced than gender discrimination and mainly occurs in jobs with favourable labour conditions or requiring greater language proficiency. In contrast, gender bias emerges in gender-atypical roles. These findings suggest that ChatGPT’s discriminatory output reflects a statistical mechanism echoing societal stereotypes. Policymakers and developers should address systemic bias in language model-driven applications to ensure equitable treatment across demographic groups. Practitioners should practice caution, given the adverse impact these tools can (re)produce, especially in selection decisions involving humans.
... Koçak, A., Derous, E., Born, M. Ph., & Duyck, W. (2023). What (not) to add in your ad: When job ads discourage older or younger job seekers to apply. ...
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This meta-analysis investigated the relationships between person–job (PJ), person–organization (PO), person–group, and person–supervisor fit with preentry (applicant attraction, job acceptance, intent to hire, job offer) and postentry individual-level criteria (attitudes, performance, withdrawal behaviors, strain, tenure). A search of published articles, conference presentations, dissertations, and working papers yielded 172 usable studies with 836 effect sizes. Nearly all of the credibility intervals did not include 0, indicating the broad generalizability of the relationships across situations. Various ways in which fit was conceptualized and measured, as well as issues of study design, were examined as moderators to these relationships in studies of PJ and PO fit. Interrelationships between the various types of fit are also meta-analyzed. 25 studies using polynomial regression as an analytic technique are reviewed separately, because of their unique approach to assessing fit. Broad themes emerging from the results are discussed to generate the implications for future research on fit.