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How do avatar characteristics affect applicants' interactional justice perceptions in artificial intelligence‐based job interviews?
Abstract
Artificial intelligence (AI)‐based job interviews are increasingly adopted in organizations' recruitment activities. Despite their standardization and flexibility, concerns about fairness for applicants remain a critical challenge. Taking a perspective on interface design, this research examines the role of avatar characteristics in shaping perceptions of interactional justice in AI‐based job interviews. Through a scenario‐based study involving 465 participants, the impact of avatar characteristics—specifically, appearance, linguistic style, and feedback informativeness—on applicants' perceptions of interpersonal justice and informational justice was investigated. The findings indicate that avatars characterized by a warm and cheerful appearance, coupled with an affective expression style and informative feedback, significantly enhance perceptions of interpersonal justice and informational justice. These insights offer valuable practical guidance for avatar design in AI‐based job interview systems.
... The ways in which job interviews take place are also changing at a fast pace. While some companies are deploying chatbots to do at least part of the task (Canagasuriam & Lukacik, 2024;Mirowska & Mesnet, 2021), others go even further by trying to give them a digital body in the shape of an avatar (Min et al., 2024). Employee monitoring (Aloisi & Gramano, 2019;Kubala et al., 2023) and performance management (Khaled et al., 2022) are also witnessing a change in the face of new AI innovations. ...
As the adoption of Artificial Intelligence (AI) tools in business goes hand in hand with technological progress, there are many key ethical issues that need to be addressed. The purpose of this article is to provide an overview of some of the most important moral implications of integrating AI systems in various business practices. The first part of our paper is dedicated to exploring the ethics of AI in the banking in financial sector. In it, we highlight some of the main difficulties faced by regulatory bodies in the sector for balancing the normative financial paradigm with a normative ethical paradigm for a responsible use of AI tools. Secondly, we focus on the ways in which Human Resource Management (HRM) has been impacted by AI, highlighting some of the main ethical issues at stake (technological unemployment, deskilling, algorithmic bias and moral responsibility). Last but not least, our paper ends with a discussion around the problems that arise the intersection of business and consumers.
... Assessment power shifts from human interviewers to AI (Suen & Hung, 2023b). AI assuming the role of decision-maker may evoke negative feelings, including indignation, in humans (Min et al., 2024;Mirowska & Mesnet, 2022). This is particularly pronounced in high-stakes situations like personnel selection. ...
AI interviews have emerged as a critical tool for businesses to perform talent screening. Interviewees frequently use impression management tactics to cope with interview stress. However, the influence of information transparency-a fundamental feature of AI system-on the selection of these tactics remains unclear. Based on transparency research and the Transactional Theory of Stress, we propose that different levels of transparency (through information type combinations) impact interviewees' perceived safety, thereby affecting their choice of impression management tactics. Furthermore, this study explores the moderating effect of AI familiarity. An experimental approach is proposed to test the conceptual model. This study contributes to the literature by establishing a taxonomy for transparency and shedding light on the nuanced effects of transparency on interviewees' perceptions and behaviors. The findings are expected to provide insights for refining AI interview systems and management practices, paving the way for future research.
Purpose
This study, leveraging impression management theory, aims to evaluate if job candidates are more likely to fake interviews when interviewed by artificial intelligence (AI) under the moderating influence of their personality and the issuance of a deception warning.
Design/methodology/approach
This study used MANCOVA, followed by mediation and moderated mediation analyses, to test the hypotheses.
Findings
This study’s findings suggest that the presence of AI as interviewing agents increases job applicants’ intention to fake job interviews, with the perceived ability of AI vs humans to detect fakeness as a mediator. The conscientiousness of job applicants and warning issuance by the Chief HR officer or CEO moderates the relationship.
Originality/value
With the advancement of information technology tools such as AI, job interviews in firms are taken over by AI more than humans. This study pioneers research into the potential for increased faking behaviour by job candidates when interacting with AI interviewers. The current study is also one of the pioneering studies, shining a light on the misuse of IT systems in human resource management practices of organisations.
Automated video interviews powered by artificial intelligence (AI) are increasingly being adopted by employers to screen job candidates, despite concerns regarding the humanity and transparency of AI. Accordingly, researchers and practitioners advocate overcoming these concerns by refining AI interfaces in terms of tangibility, immediacy, and transparency. However, AI video interviews featuring different interfaces may impact interviewees' tendencies to engage in impression management behaviors (IMs), which can either improve or impair personnel selection validity. This study addressed calls for research to investigate the issues mentioned above by conducting a field study to explore the ways in which AI and AI interfaces affect candidates' IMs in asynchronous video interviews (AVIs). We developed three AVI interfaces and measured real job applicants' self-reported IMs across four experimental treatments. We found that different AI interfaces could increase or decrease candidates' honest IMs and deceptive IMs in different ways. An exploratory analysis also found that candidates' interview anxiety could be mitigated by an AI interface.
Asynchronous video interviews (AVIs) are becoming exponentially more common in the hiring landscape. Despite practical benefits to organizations, research demonstrates potential challenges for applicants, including lower performance in technology-mediated interviews, and a host of negative attitudinal reactions to AVIs. Given this, AVI companies often provide tips for applicants, and applicants often access online resources to improve their performance. To date, we know little about interventions that can mitigate negative applicant reactions and increase applicant performance in AVIs, or the mechanisms involved in such a process. In Study 1, 202 participants from Prolific were randomly assigned to one of the four conditions (in a 2x2 experimental design) and completed a 5-question mock AVI, to explore how an AVI training video and practice impacted a host of self-report behavioral (i.e., impression management; IM) and attitudinal (i.e., anxiety, attraction, fairness, usability) outcomes, as well as response length, structure of the response, and interview performance. Results indicated that practice had negligible effects. However, training was positively associated with fairness perceptions (particularly consistency) and interview performance. Moreover, mediation analyses indicated that trained interviewees provided more structured and longer responses, which led to higher performance. Study 2 offered a replication with a sample of 156 active job seekers (senior students and Prolific users). Training was associated with more structured responses, and through this, higher performance. Pre- vs. post-training comparisons for a sub-sample also showed performance improvements. Implications, limitations, and directions for future research are discussed.
The present study examined how variations in the design of asynchronous video interviews (AVIs) impact important interviewee attitudes, behaviors, and outcomes, including perceived fairness, anxiety, impression management, and interview performance. Using a 2x2 experimental design, we investigated the impact of two common and important design elements on these outcomes: (a) preparation time (unlimited versus limited) and (b) the ability to re-record responses. Using a sample of 175 participants completing a mock AVI, we found that whereas providing such options (i.e., unlimited preparation time and/or re-recording) did not impact outcomes directly, the extent to which participants actually used these options did affect outcomes. For instance, those who used more re-recording attempts performed better in the interview and engaged in less deceptive impression management. Moreover, those who used more preparation time performed better in the interview while engaging in slightly less honest impression management. These findings point to the importance of investigating the effects of AVI design on applicant experiences and outcomes. Specifically, AVI design elements produce opportunities for applicants not typically present in synchronous interviews, and can alter interview processes in crucial ways. Finally, not all applicants use these opportunities equally, and this has implications for understanding interview behavior and outcomes.
While many organizations' hiring practices now incorporate artificial intelligence (AI) and machine learning (ML), research suggests that job applicants may react negatively toward AI/ML-based selection practices. In the current research, we thus examined how organizations might mitigate adverse reactions toward AI/ML-based selection processes. In two between-subjects experiments, we recruited online samples of participants (undergraduate students and Prolific panelists, respectively) and presented them with vignettes representing various selection systems and measured participants' reactions to them. In Study 1, we manipulated (a) whether the system was managed by a human decision-maker, by AI/ML, or a combination of both (an “augmented” approach), and (b) the selection stage (screening, final stage). Results indicated that participants generally reacted more favorably toward augmented and human-based approaches, relative to AI/ML-based approaches, and further depended on participants' pre-existing familiarity levels with AI. In Study 2, we sought to replicate our findings within a specific process (selecting hotel managers) and application method (handling interview recordings). We found again that reactions toward the augmented approach generally depended on participants’ familiarity levels with AI. Our findings have implications for how (and for whom) organizations should implement AI/ML-based practices.
We provide evidence on how workers on an online platform perceive algorithmic versus human recruitment through two incentivized experiments designed to elicit willingness to pay for human or algorithmic evaluation. In particular, we test how information on workers' performance affects their recruiter choice and whether the algorithmic recruiter is perceived as more or less gender-biased than the human one. We find that workers do perceive human and algorithmic evaluation differently, even though both recruiters are given the same inputs in our controlled setting. Specifically, human recruiters are perceived to be more error-prone evaluators and place more weight on personal characteristics, whereas algorithmic recruiters are seen as placing more weight on task performance. Consistent with these perceptions, workers with good task performance relative to others prefer algorithmic evaluation, whereas those with lower task performance prefer human evaluation. We also find suggestive evidence that perceived differences in gender bias drive preferences for human versus algorithmic recruitment.
In search of greater resource savings and efficiencies, companies are turning to new technologies in the interview process, such as artificial intelligence evaluation (AIE). However, little is known about candidate reactions to this new tool. We identified outstanding questions regarding reactions to AIE arising from justice and signaling theories and conducted interviews with 33 professionals to understand their perceptions of AIE use in selection. Participants raised issues related to all four types of justice and indicated that there is a signaling effect of AIE use. Despite acknowledging the superior objectivity of AIE, participants expressed a desire for the maintenance of human elements in the evaluation process, seemingly preferring ‘the devil they know’ (human biases and intuition) rather than the one they do not (AIE algorithm). This result is explored through the lens of uncertainty reduction, discussing theoretical implications for justice and signaling theories, and providing implications for the implementation of AIE in the selection process.
Advances in artificial intelligence are increasingly leading to the automation and augmentation of decision processes in work contexts. Although research originally generally focused upon decision-makers, the perspective of those targeted by automated or augmented decisions (whom we call “second parties”) and parties who observe the effects of such decisions (whom we call “third parties”) is now growing in importance and attention. We review the expanding literature investigating reactions to automated and augmented decision-making by second and third parties. Specifically, we explore attitude (e.g., evaluations of trustworthiness), perception (e.g., fairness perceptions), and behavior (e.g., reverse engineering of automated decision processes) outcomes of second and third parties. Additionally, we explore how characteristics of the a) decision-making process, b) system, c) second and third party, d) task, and e) outputs and outcomes moderate these effects, and provide recommendation for future research. Our review summarizes the state of the literature in these domains, concluding a) that reactions to automated decisions differ across situations in which there is remaining human decision control (i.e., augmentation contexts), b) that system design choices (e.g., transparency) are important but underresearched, and c) that the generalizability of findings might suffer from excessive reliance on specific research methodologies (e.g., vignette studies).
Organizations are increasingly adopting automated video interviews (AVIs) to screen job applicants despite a paucity of research on their reliability, validity, and generalizability. In this study, we address this gap by developing AVIs that use verbal, paraverbal, and nonverbal behaviors extracted from video interviews to assess Big Five personality traits. We developed and validated machine learning models within (using nested cross-validation) and across three separate samples of mock video interviews (total N = 1,073). Also, we examined their test–retest reliability in a fourth sample (N = 99). In general, we found that the AVI personality assessments exhibited stronger evidence of validity when they were trained on interviewer-reports rather than self-reports. When cross-validated in the other samples, AVI personality assessments trained on interviewer-reports had mixed evidence of reliability, exhibited consistent convergent and discriminant relations, used predictors that appear to be conceptually relevant to the focal traits, and predicted academic outcomes. On the other hand, there was little evidence of reliability or validity for the AVIs trained on self-reports. We discuss the implications for future work on AVIs and personality theory, and provide practical recommendations for the vendors marketing such approaches and organizations considering adopting them.
It is well-established in the literature that biases (e. g., related to body size, ethnicity, race etc.) can occur during the employment interview and that applicants' fairness perceptions related to selection procedures can influence attitudes, intentions, and behaviors toward the recruiting organization. This study explores how social robotics may affect this situation. Using an online, video vignette-based experimental survey (n = 235), the study examines applicant fairness perceptions of two types of job interviews: a face-to-face and a robot-mediated interview. To reduce the risk of socially desirable responses, desensitize the topic, and detect any inconsistencies in the respondents' reactions to vignette scenarios, the study employs a first-person and a third-person perspective. In the robot-mediated interview, two teleoperated robots are used as fair proxies for the applicant and the interviewer, thus providing symmetrical visual anonymity unlike prior research that relied on asymmetrical anonymity, in which only one party was anonymized. This design is intended to eliminate visual cues that typically cause implicit biases and discrimination of applicants, but also to prevent biasing the interviewer's assessment through impression management tactics typically used by applicants. We hypothesize that fairness perception (i.e., procedural fairness and interactional fairness) and behavioral intentions (i.e., intentions of job acceptance, reapplication intentions, and recommendation intentions) will be higher in a robot-mediated job interview than in a face-to-face job interview, and that this effect will be stronger for introvert applicants. The study shows, contrary to our expectations, that the face-to-face interview is perceived as fairer, and that the applicant's personality (introvert vs. extravert) does not affect this perception. We discuss this finding and its implications, and address avenues for future research.
Asynchronous video interviews (AVIs) are a form of one-way, technology-mediated,
selection interviewing that continue to grow in popularity. An AVI is a broad method that varies substantially in design and execution. Despite being adopted by many organizations, human resources professionals, and hiring managers, research on AVIs is lagging far behind practice. Empirical evidence is scarce and conceptual work to guide research efforts and best practice recommendations is lacking. We propose a framework for examining the role and impact of specific design features of AVIs, building on theories of justice-based applicant reactions, social presence, interview anxiety, and impression management. More precisely, our framework highlights how pre-interview design decisions by organizations and completion decisions by applicants can influence reactions and behaviors during the interview, as well as post-interview outcomes. As such, we offer an agenda of the central topics that need to be addressed, and a set of testable propositions to guide future research.
In this article, we examine the state-of-the-art and current applications of artificial intelligence (AI), specifically for human resources (HR). We study whether, due to the experimental state of the algorithms used and the nature of training and test samples, a further control and auditing in the research community is necessary to guarantee fair and accurate results. In particular, we identify the positive and negative consequences of the usage of video-interview analysis via AI in recruiting processes as well as the main machine learning techniques used and their degrees of efficiency. We focus on some controversial characteristics that could lead to ethical and legal consequences for candidates, companies and states regarding discrimination in the job market (e.g. gender and race). There is a lack of regulation and a need for external and neutral auditing for the type of analyses done in interviews. We present a multi-agent architecture that aims at total legal compliance and more effective HR processes management.
Embodied conversational agents may be used to engage users in adopting eHealth applications. The aim of this research is to investigate which design features establish a positive first impression of an agent in this context. A set of eight static agent images, different in age, gender and role, were subjected to testing in an online questionnaire. Respondents (n = 155) selected their preferred design and rated the characteristics – friendliness, expertise, reliability, involvement and authority – and the likeliness of following the agent’s advice for all designs. In addition, focus groups (n = 13) were conducted for detailed understandings supporting these impressions. Our results show that, for both a general and elderly population, (1) people seem to prefer images of young, female agents over old, male agents, (2) the (a) age, (b) gender and (c) role of the agent image affect the perception of the agent’s characteristics and the likeliness of following the agent’s advice, and that (3) both the general and elderly population prefer an agent image that is similar in (a) age and (b) gender. A next step would be to investigate how the characteristics of the agent designs are perceived after interaction with the agent.
Advances in artificial intelligence (AI) frame opportunities and challenges for user interface design. Principles for human-AI interaction have been discussed in the human-computer interaction community for over two decades, but more study and innovation are needed in light of advances in AI and the growing uses of AI technologies in human-facing applications. We propose 18 generally applicable design guidelines for human-AI interaction. These guidelines are validated through multiple rounds of evaluation including a user study with 49 design practitioners who tested the guidelines against 20 popular AI-infused products. The results verify the relevance of the guidelines over a spectrum of interaction scenarios and reveal gaps in our knowledge, highlighting opportunities for further research. Based on the evaluations, we believe the set of design guidelines can serve as a resource to practitioners working on the design of applications and features that harness AI technologies, and to researchers interested in the further development of human-AI interaction design principles.
We present artificial intelligent (AI) agents that act as interviewers to engage with a user in a text-based conversation and automatically infer the user's personality traits. We investigate how the personality of an AI interviewer and the inferred personality of a user influences the user's trust in the AI interviewer from two perspectives: the user's willingness to confide in and listen to an AI interviewer. We have developed two AI interviewers with distinct personalities and deployed them in a series of real-world events. We present findings from four such deployments involving 1,280 users, including 606 actual job applicants. Notably, users are more willing to confide in and listen to an AI interviewer with a serious, assertive personality in a high-stakes job interview. Moreover, users’ personality traits, inferred from their chat text, along with interview context, influence their perception of and their willingness to confide in and listen to an AI interviewer. Finally, we discuss the design implications of our work on building hyper-personalized, intelligent agents.
Does wearing glasses hurt or help politicians in elections? Although some research shows that glasses signal unattractiveness, glasses also increase perceptions of competence. In eight studies, participants voted for politicians wearing (photoshopped) glasses or not. Wearing glasses increased politicians’ electoral success in the US (Study 1), independent of their political orientation (Studies 2a and 2b). This positive effect was especially strong when intelligence was important (Study 3), and even occurred if glasses were used strategically (Study 4). However, it did not extend to India (Study 5) due to different cultural associations with glasses (Study 6). Furthermore, while intelligence mediated the effect, warmth did not (Study 7). In summary, wearing glasses can robustly boost electoral success, at least in Western cultures.
Purpose
As technology-created gamers’ representations, avatars are influential in communication among online gamers. However, there is scant research on how avatars’ characteristics impact gamers’ friendly behaviour via avatars, i.e., avatar friendliness, and how avatar friendliness is related to online gamer loyalty. In this study, we develop a research model grounded in the theory of embodied cognition to examine the impacts of perceived avatar appearance agreeableness, attractiveness, and height, on avatar friendliness and online gamer loyalty.
Design/methodology/approach
We collect 1,384 responses from online gamers and use structural equation modelling for hypothesis testing.
Findings
We find that perceived avatar appearance agreeableness and attractiveness are positively related to avatar friendliness, while perceived avatar height is negatively related to avatar friendliness. Avatar friendliness, in turn, is positively related to online gamer loyalty.
Research limitations/implications
This study assessed gamers’ perceptions using a cross-sectional design. Future works could use a big-data approach to collect behavioural and longitudinal data. Moreover, future works could measure avatar height using pixels.
Originality/value
We contribute to the e-commerce literature by inventing the new constructs of perceived avatar appearance agreeableness and avatar friendliness, and conducting the first study of using avatar friendliness to explain the impacts of the three avatar characteristics on online gamer loyalty. Our findings also provide novel insights for e-commerce managers to effectively build a loyal gamer base.
This study explores the dimensionality of organizational justice and provides evidence of construct validityfor a new justice measure. Items for this measure were generated by strictly following the seminal works in the justice literature. The measure was then validated in 2 separate studies. Study 1 occurred in a university setting, and Study 2 occurred in a field setting using employees in an automobile parts manufacturing company. Confirmatory factor analyses supported a 4-factor structure to the measure, with distributive, procedural, interpersonal, and informational justice as distinct dimensions. This solution fit the data significantly better than a 2- or 3-factor solution using larger interactional or procedural dimensions. Structural equation modeling also demonstrated predictive validity for the justice dimensions on important outcomes, including leader evaluation, rule compliance, commitment, and helping behavior.
The field of organizational justice continues to be marked by several important research questions, including the size of relationships among justice dimensions, the relative importance of different justice criteria, and the unique effects of justice dimensions on key outcomes. To address such questions, the authors conducted a meta-analytic review of 183 justice studies. The results suggest that although different justice dimensions are moderately to highly related, they contribute incremental variance explained in fairness perceptions. The results also illustrate the overall and unique relationships among distributive, procedural, interpersonal, and informational justice and several organizational outcomes (e.g., job satisfaction, organizational commitment, evaluation of authority, organizational citizenship behavior, withdrawal, performance). These findings are reviewed in terms of their implications for future research on organizational justice.
Digital interviews (or Asynchronous Video Interviews) are a potentially efficient new form of selection interviews, in which interviewees digitally record their answers. Using Potosky's framework of media attributes, we compared them to videoconference interviews. Participants (N = 113) were randomly assigned to a videoconference or a digital interview and subsequently answered applicant reaction questionnaires. Raters evaluated participants' interview performance. Participants considered digital interviews to be creepier and less personal, and reported that they induced more privacy concerns. No difference was found regarding organizational attractiveness. Compared to videoconference interviews, participants in digital interviews received better interview ratings. These results warn organizations that using digital interviews might cause applicants to self-select out. Furthermore, organizations should stick to either videoconference or digital interviews within a selection stage.
Technologically advanced selection procedures are entering the market at exponential rates. The current study tested two previously held assumptions: (a) providing applicants with procedural information (i.e., making the procedure more transparent and justifying the use of this procedure) on novel technologies for personnel selection would positively impact applicant reactions, and (b) technologically advanced procedures might differentially affect applicants with different levels of computer experience. In a 2 (computer science students, other students) × 2 (low information, high information) design, 120 participants watched a video showing a technologically advanced selection procedure (i.e., an interview with a virtual character responding and adapting to applicants’ nonverbal behavior). Results showed that computer experience did not affect applicant reactions. Information had a positive indirect effect on overall organizational attractiveness via open treatment and information known. This positive indirect effect was counterbalanced by a direct negative effect of information on overall organizational attractiveness. This study suggests that computer experience does not affect applicant reactions to novel technologies for personnel selection, and that organizations should be cautious about providing applicants with information when using technologically advanced procedures as information can be a double-edged sword.
Update: While not specifically mentioned in the paper it has implications for explainability and XAI research: providing people with more transparency can have simultaneous positive and negative effects on acceptance.
We present an intelligent virtual interviewer that engages with a user in a text-based conversation and automatically infers the user's psychological traits, such as personality. We investigate how the personality of a virtual interviewer influences a user's behavior from two perspectives: the user's willingness to confide in, and listen to, a virtual interviewer. We have developed two virtual interviewers with distinct personalities and deployed them in a real-world recruiting event. We present findings from completed interviews with 316 actual job applicants. Notably, users are more willing to confide in and listen to a virtual interviewer with a serious, assertive personality. Moreover, users' personality traits, inferred from their chat text, influence their perception of a virtual interviewer, and their willingness to confide in and listen to a virtual interviewer. Finally, we discuss the implications of our work on building hyper- personalized, intelligent agents based on user traits.
Many individuals now meet and develop friendships while online. As a result, people must form impressions of online acquaintances based on that person's online representation. Here, we investigate personality inferences and intentions to befriend based solely on simple avatars (i.e., customized cartoon representations of the self). Our data show that some traits are more easily inferred from avatars than others, avatars can communicate accurate and distinctive information regarding personality, and individuals with certain personality traits create avatars that are more likely to be perceived accurately. We also found that agreeable and normative individuals created avatars that elicited more desire for friendship from others, implying that the impression given by one's digital avatars may have social consequences.
© 2014 by the Society for Personality and Social Psychology, Inc.
While a great deal of research has investigated strategies for increasing job seekersí initial attraction
to organizations, far less is known about how job seekers respond to recruitment activities after
application submission. We draw from signaling, uncertainty reduction, and uncertainty management
theories to develop a conceptual model of the relationship between recruitment interactions (contact
episodes) after application submission and organizational attraction. We test this model in three
independent studies with data collected at multiple time periods. Study 1 employed a time-lagged
research design with actual job seekers. Findings showed that justice perceptions associated with
recruitment interactions influence organizational attraction indirectly and directly via positive
relational certainty (i.e., reduced uncertainty regarding how organizational relations might be upon
entering the organization). Study 2 used a controlled experimental design to provide additional
evidence of the relational certainty mechanism through which justice signals influence attraction.
Finally, Study 3 incorporated a longitudinal (repeated measures) design to examine reactions to
recruitment interactions across 10 weeks. Results indicated that the relationship between justice
signals and organizational attraction via positive relational certainty is dynamic in nature, suggesting
that organizations should carefully manage their communications throughout the recruitment process.
This study examined reactions to different types of interviews. It used a 2 × 2 × 2 between-subjects design in which interview script was manipulated in terms of interview structure, interviewer warmth, and interview outcome. Eight job interviews were videotaped using the same 2 people to role-play the job applicant and the interviewer. Undergraduate psychology students participated in this study (N = 257), and were randomly assigned to 1 of the 8 conditions. Participants watched the videos and then completed a questionnaire measuring organizational attractiveness, recommendation intentions, whether they would accept a job offer, and perceptions of organizational justice. The main findings were a significant 3-way interaction found between interview outcome, interview structure, and warmth of the interviewer for accepting a job offer; as well as mediating effects of procedural and interactional justice on the relationship between interviewer warmth on recommendation intentions, organizational attractiveness, and accepting a job offer. Implications for organizational practice include incorporating interviewer warmth into the structured interview process, as well as hiring recruiters who demonstrate warm behaviors. This way, organizations might concurrently make optimal selection decisions, while also creating positive and fair impressions of the organization to applicants. (PsycINFO Database Record (c) 2013 APA, all rights reserved)
Job interviews come with a number of challenges, especially for young people who are out of employment, education, or training (NEETs). This paper presents an approach to a job training simulation environment that employs two virtual characters and social cue recognition techniques to create an immersive interactive job interview. The two virtual characters are created with different social behavior profiles, understanding and demanding, which consequently influences the level of difficulty of the simulation as well as the impact on the user. Finally, we present a user study which investigates the feasibility of the proposed approach by measuring the effect the different virtual characters have on the users.
This book describes research in all aspects of the design, implementation, and evaluation of embodied conversational agents as well as details of specific working systems.
Embodied conversational agents are computer-generated cartoonlike characters that demonstrate many of the same properties as humans in face-to-face conversation, including the ability to produce and respond to verbal and nonverbal communication. They constitute a type of (a) multimodal interface where the modalities are those natural to human conversation: speech, facial displays, hand gestures, and body stance; (b) software agent, insofar as they represent the computer in an interaction with a human or represent their human users in a computational environment (as avatars, for example); and (c) dialogue system where both verbal and nonverbal devices advance and regulate the dialogue between the user and the computer. With an embodied conversational agent, the visual dimension of interacting with an animated character on a screen plays an intrinsic role. Not just pretty pictures, the graphics display visual features of conversation in the same way that the face and hands do in face-to-face conversation among humans. This book describes research in all aspects of the design, implementation, and evaluation of embodied conversational agents as well as details of specific working systems. Many of the chapters are written by multidisciplinary teams of psychologists, linguists, computer scientists, artists, and researchers in interface design. The authors include Elisabeth Andre, Norm Badler, Gene Ball, Justine Cassell, Elizabeth Churchill, James Lester, Dominic Massaro, Cliff Nass, Sharon Oviatt, Isabella Poggi, Jeff Rickel, and Greg Sanders.
Technology‐enabled recruitment and selection technologies, such as chatbots, assessment games, and asynchronous video interviews, are becoming more widely used. However, their impact on people with disabilities is frequently ignored; this has potentially significant implications for the perceived fairness of hiring decisions. We advance eight theoretical propositions on the positive and negative implications of technology‐enabled recruitment and selection technologies for applicant reactions of people with disabilities. Our propositions are based on three key design features of these technologies: separation in time and space, automated administration, and automated evaluation. We provide recommendations for future research and discuss practical implications for the use of advanced technology in recruitment and selection.
As the demand for automatic video interviews powered by artificial intelligence (AI) increases among employers in the postpandemic era, so do concerns over job applicants' trust in the technology. There are various forms of AI-based video interviews with and without the features of tangibility, immediacy, and transparency used for preemployment screening, and these features may distinctively influence applicants' trust in the technology and whether they engage in or disengage from the hiring process accordingly. This field study involved designing a test of the effect of various forms of AI-based video interviews on interviewees' cognitive and affective trust based on the self-reporting of 152 real job applicants. The study found that AI used in asynchronous video interviews (AI-AVI) increased applicants' cognitive trust from that in the non-AI condition. Moreover, when the AI-AVI had features of tangibility and transparency, the applicants’ cognitive and affective trust increased. However, the feature of immediacy did not have a statistically significant impact. Contrary to concern over the potential negative effects caused by AI and its features, no statistically significant impacts were found in this study.
Effective public engagement with complex technologies requires a nuanced understanding of how different audiences make sense of and communicate disruptive technologies with immense social implications. Using latent class analysis (LCA) on nationally-representative survey data (N = 2,700), we examine public attitudes on different aspects of AI, and segment the U.S. population based on their AI-related risks and benefit perceptions. Our analysis reveals five segments: the negative, perceiving risks outweighing benefits; the ambivalent, seeing high risks and benefits; the tepid, perceiving slightly more benefits than risks; the ambiguous, perceiving moderate risks and benefits; and the indifferent, perceiving low risks and benefits. For societal debates surrounding a deeply disruptive issue like AI, our findings suggest potential opportunities for engagement by soliciting input from individuals in segments with varying levels of support for AI, as well as a way to widen representation of voices and ensure responsible innovation of AI.
Artificial intelligence (AI) is increasingly used in personnel selection to automate decision-making. Initial evidence points to negative effects of automating these procedures on applicant experiences. However, the effect of the prospect of automated procedures on job-seekers’ pre-process perceptions (e.g., organizational attractiveness) and intentions (to apply for the advertised job) is still unclear.
We conducted three experiments (Study 1 and Study 2 as within-subjects designs, Study 3 as a between-subjects design; N1 = 36, N2 = 44, N3 = 172) systematically varying the information in job advertisements on the automation of different stages of the selection process (Study 1: screening stage conducted by a human vs. a non-specified agent vs. an AI; Study 2 and Study 3: human screening and human interview vs. AI screening and human interview vs. AI screening and AI interview).
Results showed small negative effects of screening conducted by an AI vs. a human (Study 1, Study 2, Study 3), but stronger negative effects when also interviews were conducted by an AI vs. a human (Study 2, Study3) on job-seekers pre-process expectations, perceptions, and intentions.
Possible reasons for these effects are discussed with special consideration of the different stages of the recruiting and selection process and explored with a qualitative approach in Study 2.
Artificial intelligence (AI) is increasingly being utilized by organizations in selection decisions. However, research has fallen behind the practice, and one area in need of investigation is how applicants' perceptions of justice are formed in this increased involvement of AI in the hiring process. Accordingly, two studies were conducted to investigate the effects of using AI in selection on justice perceptions. Findings indicated that AI‐based interviewing was generally viewed as less procedurally and interactionally just than traditional human‐based interviewing. Additionally, the effect of interview type on different applicant reaction outcomes was mediated by justice dimensions, particularly two‐way communication. Findings may help organizations regarding how best to utilize AI in selection in order to attract and retain top talent.
Artificial Intelligence (AI) characterizes a new generation of technologies capable of
interacting with the environment and aiming to simulate human intelligence. The success of integrating AI into organizations critically depends on workers’ trust in AI technology. This review explains how AI differs from other technologies and presents the existing empirical research on the determinants of human trust in AI, conducted in multiple disciplines over the last twenty years.
Based on the reviewed literature, we identify the form of AI representation (robot, virtual,
embedded) and the level of AI’s machine intelligence (i.e. its capabilities) as important antecedents to the development of trust and propose a framework that addresses the elements that shape users’ cognitive and emotional trust. Our review reveals the important role of AI’s tangibility, transparency, reliability and immediacy behaviors in developing cognitive trust, and the role of AI’s anthropomorphism specifically for emotional trust. We also note several limitations in the current evidence base, such as diversity of trust measures and over-reliance on short-term, small sample, and experimental studies, where the development of trust is likely to be different than in
longer term, higher-stakes field environments. Based on our review, we suggest the most
promising paths for future research.
AI-enabled recruiting systems have evolved from nice to talk about to necessary to utilize. In this article, we outline the reasons underlying this development. First, as competitive advantages have shifted from tangible to intangible assets, human capital has transitioned from supporting cast to a starring role. Second, as digitalization has redesigned both the business and social landscapes, digital recruiting of human capital has moved from the periphery to center stage. Third, recent and near-future advances in AI-enabled recruiting have improved recruiting efficiency to the point that managers ignore them or procrastinate their utilization at their own peril. In addition to explaining the forces that have pushed AI-enabled recruiting systems from nice to necessary, we outline the key strategic steps managers need to take in order to capture its main benefits.
Recruiting talent has moved from a tactical HR activity to a strategic business priority. This has been driven by shifts in the source of firm value and competitive advantage and the critical role of human capital in those shifts. Technological advances have moved digital, AI-enabled recruiting from a peripheral curiosity to a critical capability. However, we know little about candidates' reactions to AI-enabled recruiting. Consequently, in this study, we examine the role of social media use, intrinsic rewards, fair treatment, and perceived trendiness on the intentions of prospective employees to engage with and complete digital, AI-enabled recruiting processes. The positive relationships between these factors and candidates' engagement with AI-enabled recruiting have several important practical implications for managers. We also examine the larger implications and make general recommendations to firms about using AI-enabled recruiting technology and tools.
In this paper, we focus on experience-based role play with virtual agents to provide young adults at the risk of exclusion with social skill training. We present a scenario-based serious game simulation platform. It comes with a social signal interpretation component, a scripted and autonomous agent dialog and social interaction behavior model, and an engine for 3D rendering of life-like virtual social agents in a virtual environment. We show how two training systems developed on the basis of this simulation platform can be used to educate people in showing appropriate socio-emotive reactions in job interviews. Furthermore, we give an overview of four conducted studies investigating the effect of the agents' portrayed personality and the appearance of the environment on the players' perception of the characters and the learning experience.
Although it is a common practice for organizations to communicate with job seekers following application submission, little is known about how applicants react to this correspondence. Drawing from recruitment and organizational justice theories, we explore the possibility that specific correspondence content influences job seekers’ fairness perceptions. Data collected from 119 actual job applicants indicated that providing relevant information about the recruitment process (information adequacy) positively related to informational and interpersonal justice perceptions. However, delivering this information in an interpersonally sensitive manner (information sensitivity) had a stronger impact on interpersonal justice perceptions. Finally, post hoc analyses suggested that incorporating specific content delivered in initial job applicant correspondence could allow recruiting organizations to develop practical, cost-effective strategies for enhancing job seekers’ fairness perceptions following their application submission. © 2014 Wiley Periodicals, Inc.
The aim of the two studies presented here was to add to our knowledge about the contribution of facial expression to the perception of leadership. We assessed participants' prototypes of leadership. In addition, participants were shown pictures of different facial expressions. First impressions of leadership from the facial expressions were compared to the participants' prototypes. The results indicate that the participants used all available information, including facial appearance, expression, context of communication, appropriateness, and authenticity of expression to form complex prototypes. When the facial expressions in the studies matched the participants' prototypes, first impressions of leadership were higher. Therefore, understanding what is inside the perceiver's mind is significant for understanding leadership perceptions. On the basis of these two studies, we recommend that leaders should be aware of the influence their facial expressions have on their followers' perception of their leader-likeness.
The effects of explanation features on participants' reactions toward a selection decision were examined in 2 studies. In Study 1, students were provided with scenarios where informational (justification, procedural, personal, control) and sensitivity (sensitive or control) features of explanations were crossed with a selection decision to assess their effects on 3 applicant reactions: process fairness, self-perceptions, and organizational perceptions. In general, personal information enhanced fairness and organizational perceptions but harmed the reported self-perceptions of students role-playing rejected applicants. Explanations given in a sensitive manner accentuated these effects. Study 2 used a similar methodology to assess the effects of giving different types of procedural information. Self-reported reactions were influenced by the interactive effects of the type of procedural information provided and the selection decision. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Applicants' reactions to selection procedures were examined in terms of the satisfaction and/or violation of 10 procedural justice rules. Critical incidents (n= 237) of fair and unfair treatment during selection were collected from 31 individuals who had recently experienced job search and hiring processes. Incidents were categorized into 10 procedural justice rules and the distribution of these incidents was examined for different hiring outcomes and different selection procedures. Dominant procedural concerns reflected selection procedure job relatedness and interpersonal treatment applicants received. Accepted applicants were primarily concerned about consistency of treatment, while rejected applicants were more concerned with timely feedback and blatant bias. Ease of faking was the primary procedural concern of applicants taking honesty and personality tests, while job relatedness was the primary concern with ability and work sample tests. Research issues were discussed and a number of practical suggestions were offered in terms of minimizing applicants' negative reactions to the selection process.
Demonstrating procedural justice through a focus on the psychometric job-related approach to selection continues to be the most effective means for employers to meet legal requirements and potential claims of unfair treatment. A study of selection practice in a large local UK City Council reports how a structured, highly ‘job-focused’ approach can result in negative perceptions about the fairness of the process, its outcomes and effectiveness. Its findings reveal an organizational dilemma – how to develop selection systems that are sufficiently robust in terms of demonstrating maximum procedural fairness and objectivity to withstand potential litigation but are sufficiently flexible to accommodate those other factors which influence individual perceptions of fairness. It considers the future of the highly structured approach in the light of pressures to develop selection processes which can meet the needs of rapidly changing organizational structures as well the expansion of anti-discrimination legislation and litigation.