Access to this full-text is provided by Frontiers.
Content available from Frontiers in Robotics and AI
This content is subject to copyright.
ORIGINAL RESEARCH
published: 11 November 2020
doi: 10.3389/frobt.2020.586263
Frontiers in Robotics and AI | www.frontiersin.org 1November 2020 | Volume 7 | Article 586263
Edited by:
Alessandra Sciutti,
Italian Institute of Technology (IIT), Italy
Reviewed by:
Hideyuki Nakanishi,
Osaka University, Japan
Sonja Kristine Ötting,
Bielefeld University, Germany
*Correspondence:
Sladjana Nørskov
norskov@btech.au.dk
Specialty section:
This article was submitted to
Human-Robot Interaction,
a section of the journal
Frontiers in Robotics and AI
Received: 22 July 2020
Accepted: 07 October 2020
Published: 11 November 2020
Citation:
Nørskov S, Damholdt MF, Ulhøi JP,
Jensen MB, Ess C and Seibt J (2020)
Applicant Fairness Perceptions of a
Robot-Mediated Job Interview: A
Video Vignette-Based Experimental
Survey. Front. Robot. AI 7:586263.
doi: 10.3389/frobt.2020.586263
Applicant Fairness Perceptions of a
Robot-Mediated Job Interview: A
Video Vignette-Based Experimental
Survey
Sladjana Nørskov 1
*, Malene F. Damholdt2, John P. Ulhøi3, Morten B. Jensen 4,
Charles Ess 5and Johanna Seibt 6
1Department of Business Development and Technology, Aarhus University, Herning, Denmark, 2Department of Psychology
and Behavioral Sciences, Aarhus University, Aarhus, Denmark, 3Department of Management, Aarhus University, Aarhus,
Denmark, 4Department of Economics and Business Economics, Aarhus University, Aarhus, Denmark, 5Department of Media
and Communication, University of Oslo, Oslo, Norway, 6Department of Philosophy and History of Ideas, Aarhus University,
Aarhus, Denmark
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.
Keywords: robot-mediated interview, fairness perceptions, implicit biases, fair proxy, job interview
Nørskov et al. Robot-Mediated Interview and Fairness
INTRODUCTION
Personnel selection is one of the organizational activities that has
experienced an increasing digitalization in the last few decades
(Woods et al., 2020). Methods such as online applications (Sylva
and Mol, 2009), digital interviews (Langer et al., 2017), and
gamified assessments (Hawkes et al., 2018) have been found to
provide practitioners with easier and faster selection procedures
(Woods et al., 2020). While it has been argued that such digital
selection procedures may be able to reduce implicit biases in
applicant selection (Suen et al., 2019), research suggests that
these methods may also replicate some of the biases from the
traditional selection procedures (Lievens et al., 2015). To avoid
replication of biases, recent research argues that the setup of
the selection procedure may require change. In the context of
job interview, Seibt and Vestergaard (2018) propose using a
teleoperated robot as a fair proxy that removes visual cues (race,
ethnicity, body size, etc.) that typically trigger implicit biases. This
paper therefore empirically tests the fairness perceptions of using
a robotic proxy in job interviews.
The employment interview is a critical organizational activity
that helps organizations in recruiting the necessary workforce.
It enjoys a widespread recognition among applicants and
organizational decision-makers as one of the most commonly
used selection techniques to assess candidates for employment
(Macan, 2009). Significant executive resources and formalized
efforts are allocated to identifying the “best” applicants by,
for example, using well-documented tests based on scientific
standards and evidence followed up by formal and (to varying
degrees) structured interviews. Despite its long-held criticality in
personnel selection, the employment interview has been found to
be under the influence of implicit and potentially discriminating
biases (Purkiss et al., 2006; García et al., 2008). Implicit biases
involve rapid and automatic processing of information, which
occurs unconsciously and tends to be difficult to control and
change (Hinton, 2017). Holroyd (2012) provides an illustrative
example of how implicit biases may work: “An individual harbors
an implicit bias against some stigmatized group (G), when she
has automatic cognitive or affective associations between (her
concept of) G and some negative property (P) or stereotypic
trait (T), which are accessible and can be operative in influencing
judgment and behavior without the conscious awareness of
the agent” (p. 275). For instance, in the case of employment
interviews, implicit associations an interviewer may have related
to physical appearance, obesity, race, and gender are some of
the factors known to influence the way applicants are perceived
and evaluated, thus resulting in the bias being manifested
unintentionally (e.g., Heilman and Saruwatari, 1979; Johnson
et al., 2010; Grant and Mizzy, 2014; Ruffle and Shtudiner, 2015).
Allowing such biases into the selection process may reduce
employee diversity and harm organizational reputation as well
as organizational creativity and financial performance (Homan
et al., 2007; Hewlett et al., 2013).
The subtle, unconscious effects of biases are not easy to
deal with, and knowledge of how to effectively control and
change biases is still incipient (Forscher and Devine, 2015).
Extant research indicates that while implicit biases may be
malleable, they tend to be highly resistant to change (Amodio,
2014). In a series of nine interventions, Lai et al. (2016) found
that while the interventions were able to reduce implicit biases
immediately, this effect was short-lived, i.e., it lasted between
a few hours and a few days. Similarly, diversity training that
is intended to make managers unbiased and inclusive has been
shown to only have short-term effects (Dobbin and Kalev,
2016). It has further been argued that implicit biases are more
likely to have a stronger effect during interactions between
strangers than between acquaintances and friends (Landy, 2008).
As the employment interview is most often an encounter
between individuals who do not know each other, it is thus
particularly exposed to implicit biases. In addition, interviewers
commonly rely on intuition in their assessment and selection
of applicants that may bias the selection decision. Even though
the evidence suggests that intuitive selection is an inferior
predictor of employee performance compared with analytical
selection, research shows that HR professionals insist on relying
on intuition in personnel selection, and in job interviews in
particular (Highhouse, 2008).
As a response to the lack of effective means to control implicit
biases in job interviews, we examine whether robots could be
used to alleviate such biases. The goal is to test the potential
of robots to increase objectivity and fairness in job interviews
by using them as a fair proxy communication (FPC) device to
“remove perceptual cues of implicit biases in order to increase the
perceived fairness of decision-related communications” (Seibt
and Vestergaard, 2018, p. 1). By eliminating some of the known
triggers of implicit biases, such as visual cues that reveal physical
appearance, age, ethnicity, gender, and even emotional states,
a robotic proxy may hold the potential to create a fairer
situation for applicants compared with a traditional face-to-face
job interview (Nørskov and Ulhøi, 2020). While research shows
that job interviews conducted via video conference or telephone
are perceived as less fair than face-to-face interviews (Sears
et al., 2013), the unique characteristics of a physically present
robot may affect communication diferently when compared to
a video conference and/or a telephone conference. Embodied
and physically present agents in an interaction have indeed
been found to be more engaging and capable of eliciting more
favorable psychological responses (e.g., trust, empathy) and a
greater sense of social presence compared to communication via
a screen or a telephone (Li, 2015; Seo et al., 2015). If, in addition
to these advantages, a teleoperated robot as a fair proxy is able
to reduce or eliminate biases from the job interview, it seems
reasonable to expect that a robot-mediated interview could yield
higher perceptions of fairness than a face-to-face job interview.
Our study thus examines whether a teleoperated robot could be
used to mediate job interviews and increase applicants’ fairness
perceptions (i.e., procedural and interactional fairness) of the
interview process and their behavioral intentions (i.e., intentions
of job acceptance, reapplication intentions, and recommendation
intentions), and whether applicant personality (introvert vs.
extravert) affects this relationship. We investigate this question
by employing an online vignette-based experimental survey from
a first-person and a third-person perspective (for elaboration see
section Procedures).
Frontiers in Robotics and AI | www.frontiersin.org 2November 2020 | Volume 7 | Article 586263
Nørskov et al. Robot-Mediated Interview and Fairness
The paper makes two main contributions. Firstly, it tests
a different conceptualization of FPC, than existing conceptual
research has proposed, namely a symmetrical visual anonymity
as opposed to the asymmetrical visual anonymity that was
only recently conceptualized (Seibt and Vestergaard, 2018) and
empirically tested (Adrian et al., 2019). When the robot-mediated
job interview is based on the asymmetrical visual anonymity,
it allows the applicant to see the interviewer via a computer
screen, while the applicant is represented by a teleoperated robot.
Such a setup seeks to create a fairer assessment situation for
the applicant by removing visual cues related to the applicant’s
physical appearance, which is expected to limit the interviewer’s
biases toward the applicant. However, by also being able to see
the interviewer and his/her nonverbal reactions, the applicant’s
may more easily engage in impression management tactics,
which are known to bias the selection process and provide a
false impression of the applicant (e.g., Cuddy et al., 2015). We
therefore argue that the symmetrical visual anonymity—in which
both parties are represented by a teleoperated robot—may serve
to alleviate this risk and may thus be fairer to both parties.
By reducing the opportunity for impression management, the
symmetrical job interview places even more emphasis on
the applicant’s knowledge, abilities and skills as the objective
criteria for applicant selection. Secondly, our study increases
the understanding of when the robot-mediated job interview
could be an effective alternative to the face-to-face job interview,
including the possible target groups for this type of interview.
In the following, we present a review of the relevant literature
and our hypotheses. We then explain our research design and
report the results of the study. Finally, we discuss the findings
and their implications.
THEORETICAL BACKGROUND AND
HYPOTHESES
Below we present the concepts of applicant fairness perceptions,
behavioral intentions, Fair Proxy Communication (FPC), and
applicant personality (introvert vs. extravert), and how they are
interrelated. To understand how robot-mediation may affect
the job interview process, we also discuss relevant literature on
technology-mediation in job interviews and in communication
allowing us to develop a set of hypotheses that the study tests.
Applicant Fairness Perceptions and
Behavioral Intentions in Job Interviews
Organizational justice deals with fairness perceptions in the
workplace (Byrne and Cropanzano, 2001) and has its origins in
social psychology research (Adams, 1965; Thibaut and Walker,
1975; Folger, 1977; Greenberg, 1987). Based on the organizational
justice persepctive, Gilliland (1993) developed a model of
applicants’ reactions to selection systems. Research on applicant
reactions investigates the “attitudes, affect, or cognitions an
individual might have about the hiring process” (Ryan and
Ployhart, 2000, p. 566). Our focus is on one particular type
of applicant reactions, namely fairness perceptions of selection
procedures. Gilliland’s (1993) model was later taken up by Bauer
et al. (2001) to develop the selection procedural justice scale for
measuring applicant fairness perceptions of personnel selection.
This scale is used to assess procedural, interactional and outcome
fairness. While procedural fairness refers to the consistency
and fairness of the applied job interview process and method,
interactional fairness refers to the job interview being conducted
in a respectful and informative way (Gilliland and Steiner, 1999).
Finally, outcome fairness is related to the fairness of the decision
that the job applicant receives from the hiring organization. In
this study, we focus on applicant fairness perceptions of the
job interview setup, and thus measure fairness only in terms of
procedural and interactional fairness (Bauer et al., 2001).
Moreover, it is well-established that fairness perceptions of
applicants have a positive effect on their behavioral intentions
(Hausknecht et al., 2004; McLarty and Whitman, 2016). The
latter construct refers to applicants’ intentions to: (i) accept
a job if offered one, (ii) recommend the organization to
other jobseekers, and (iii) apply again for a job at the
organization (Hausknecht et al., 2004; McLarty and Whitman,
2016). Behavioral intentions may thus affect an organization’s
ability to attract the best candidates (Ryan and Huth, 2008). The
recommendation and reapplication intentions of candidates may
significantly influence the quantity and the quality of prospective
applicant pools (Kluger and Rothstein, 1993), which makes
behavioral intentions important to predicting future behaviors
of applicants.
Implicit Biases in Job Interviews
Job interviews are particularly exposed to the risk of implicit
biases because they typically involve face-to-face communication
between strangers. Here, applicants’ physical appearance, race,
gender, etc. are observable and may influence how applicants are
assessed. Indeed, extant research has documented that affective
processes and subjective impressions prevail over applicants’
qualifications and skills in job interviews (Graves and Powell,
1996; García et al., 2008; Huffcutt, 2011). It is well-established
that automatic first reactions and the chemistry that emerges
during the very first moments of two people meeting each
other are sources of implicit biases (Zajonc, 1980; Howard and
Ferris, 1996). Also, cultural similarity between an applicant
and an employer increases the chances of the applicant being
hired (Rivera, 2012, 2015). Similarly, applicants are more likely
to be offered a job when the way they exhibit emotions
(i.e., calm or excited) during job interviews is similar to
the culturally-dependent expectations of interviewer (Bencharit
et al., 2018). Additional sources of implicit biases are, for
instance, the preference for interacting with people of the same
gender (i.e., homosociality) and the tendency to have relations
with people of similar sociodemographic background, personal
and behavioral characteristics (i.e., homophily) (McPherson
et al., 2001; Holgersson, 2013). Indeed, Rivera (2015) showed
that homophily was the most dominant determinant in the
assessment of applicants in job interviews. Furthermore, physical
attractiveness also exposes applicants to implicit biases (Ruffle
and Shtudiner, 2015). For example, when applying for non-
managerial positions, physically attractive women have an
advantage, while the opposite is the case for managerial positions
Frontiers in Robotics and AI | www.frontiersin.org 3November 2020 | Volume 7 | Article 586263
Nørskov et al. Robot-Mediated Interview and Fairness
(Heilman and Saruwatari, 1979). Allowing such biases to persist
is likely to reduce diversity of employees, which may cause
organizational performance and innovation as well as team
creativity and team effectiveness to suffer (Homan et al., 2007;
Hewlett et al., 2013). Designing fairer selection processes may
thus benefit both applicants and organizations.
We test whether the robot-mediated interview may be
able to eliminate visual cues and related biases and facilitate
a situation where the interview procedure is more consistent
and offers a more equal treatment of applicants than a face-
to-face interview. Such a situation may help applicants focus
on the contents of the interview and on demonstrating their
knowledge and skills, giving them a better chance to perform
(Gilliland, 1993). Indeed, Chapman and Rowe (2001) found
that applicant competency ratings received a higher grading
in videoconference-based interviews than in face-to-face
interviews. The authors speculated that having a technology-
based communication medium might have reduced applicant
anxiety resulting in higher performance (Chapman and Rowe,
2001). Feelings of weak knees or sweaty palms are well-known
reactions some applicants experience during a face-to-face
employment interview. Anxiety has, for instance, been found
to reduce hireability and job suitability ratings (Jeske et al.,
2018). The employment interview has also been documented
to be associated with applicant anxiety, which in turn affects
the predictive validity of the employment interview and
leads to the selection of less fitting applicants (McCarthy and
Goffin, 2004). Similarly, shy individuals have been found to
benefit from computer-mediated communication compared
with face-to-face communication (Stritzke et al., 2004), which
indicates a possible relevance of personality (introvert vs.
extravert) for applicant perceptions of job interview methods.
Technology-mediated communication may thus be useful
to reduce or disguise nervousness and anxiety associated
with face-to-face communication, especially for introvert
personality types.
Interaction Between the Two Main Effects
In the following three sections, i.e. Fair Proxy Communication
in Job Interviews, Behavioral Intentions and Interview Setup,
and Applicant Personality, Fairness Perceptions, Behavioral
Intentions and Interview Setup, we treat the two main
effects (robot-mediated vs. face-to-face interview and introvert
vs. extravert personality) in relation to applicant fairness
perceptions (measured as procedural and interactional fairness)
and behavioral intentions. To justify that we treat the main effects
separately, we first need to confirm H1. In order to proceed, we
follow the recommendations in the literature regarding analysis
of experimental designs (Kirk, 1995). A pre-requisite for handling
the two main effects separately is that they operate independently
of each other, i.e., that the effects of a robot-mediated vs. a face-
to-face interview do not depend on whether we are looking at an
introvert or an extravert applicant, or vice versa. Thus, we start
by hypothesizing:
H1: The two main effects can be treated separately for all
three constructs (procedural fairness, interactional fairness
and behavioral intentions).
FIGURE 1 | The four types of job interview setups with and without a fair proxy.
Fair Proxy Communication in Job
Interviews
One way to secure a fairer applicant selection process is to use
a robot as a fair proxy, an idea that is based on the principles
of FPC (Seibt and Vestergaard, 2018). FPC provides a possible
substitute for face-to-face communication and aims to improve
the fairness perceptions of communication through the use of
a robotic proxy. FPC thus pursues a practical-ethical objective
of limiting the presence of perceptual cues that are known to
trigger cognitive biases in face-to-face communication (Seibt
and Vestergaard, 2018). Figure 1 shows the four types of job
interviews and the roles FPC can play in this context. The starting
point is the traditional face-to-face job interview without visual
anonymity in quadrant I, while quadrant IV shows a situation in
which only the interviewer is visually anonymous, because she is
represented by a robotic proxy. This situation is deemed unfair to
the applicant, as it places even more power into the hands of the
interviewer by hiding her identity and non-verbal behavior, while
allowing for potential perceptual biases toward the applicant. In
between these two situations, quadrants II and III show the two
interview setups that use FPC in a potentially beneficial way, i.e.,
to create more fairness in job interviews.
The concept of FPC involves the use a fair proxy to provide an
asymmetrical visual anonymity, as it uses a robotic proxy for only
one communication partner. FPC is intended to create a fairer
situation for the applicant, who is usually exposed to perceptual
biases in face-to-face interviews (quadrant II). However, in this
study we argue that a symmetrical use of a fair proxy in job
interviews may be equally relevant to consider (quadrant III).
That is a situation in which the robot acts as a fair proxy for
both the applicant and the interviewer (Figure 2). The reason
Frontiers in Robotics and AI | www.frontiersin.org 4November 2020 | Volume 7 | Article 586263
Nørskov et al. Robot-Mediated Interview and Fairness
FIGURE 2 | The “symmetrical” robot-mediated job interview, from the perspective of the interviewer (a) and the job candidate (b). In (a), a male interviewer
communicates via the robot with a female job candidate, who is seated in another room, as shown in (b). As the interviewer and the applicant communicate, their
head movements, lip movements, and speech are transmitted via the robot.
for this is that applicants are known to engage in impression
management techniques to improve their interview performance.
Job interviews demand appropriate self-presentation to increase
the applicants’ chances of being hired (Kacmar et al., 1992; Proost
et al., 2010).
Research suggests that interviewers should be cautious during
applicant assessment, because self-presentation can be used
in such a way that interviewers neglect competences and are
swayed by the convincing self-presentation style of the applicant
(Paulhus et al., 2013). As pointed out by Buzzanell (1999),
prospective job applicants are typically advised to create the
“right” image. A review of the literature has indicated that
men and women engage in impression management tactics
in business situations that tend to reflect stereotypical gender
roles. Differently put, female organizational members tend to be
stuck in a double bind where those who portray themselves as
more communal and submissive are not chosen for leadership
positions while candidates who try to utilize male tactics (such
as being more aggressive) bear the negative consequences for
violating normative gender roles (Guadagno and Cialdini, 2007).
Studies have further shown that applicants can be trained to use
non-verbal cues, such as body posture, to improve their chances
of being hired. Cuddy et al. (2015) examined preparatory power
posing, and found that adopting a high (vs. low) power pose in
job interviews increases an applicant’s chance of being hired. Such
preparation and training for a job interview are used to manage
impressions during the job interview and may additionally bias
the selection process and provide a false impression of the
applicant. We therefore examine a symmetrical FPC setting,
which may serve to alleviate this risk.
Reducing visual cues in robot-mediated job interviews
implies a certain degree of applicant anonymity. This is
similar to telephone interviews, which have been found to
change the applicants’ and interviewers’ style and content of
communication (Harmon et al., 1995). For instance, Silvester and
Anderson (2003) found that interviewers rated applicants higher
in telephone interviews, when applicants attributed positive
outcomes (of their prior employment) to personal causes, which
was not the case in the face-to-face condition. Applicants were
also found to use more personal attributions in telephone
interviews compared to face-to-face interviews. The study also
revealed a significant gender difference in face-to-face interviews
that was not found in the telephone interviews. Male applicants
were asked significantly more open questions during the face-
to-face interviews and they talked more (i.e., produced more
discourse). These findings suggest that visual anonymity in an
interview shapes how the applicants present themselves, but
also what information the interviewer will pay attention to, and
consequently how the applicant will be assessed.
In their examination of text-based computer-mediated
communication, Tidwell and Walther (2002) let dyads either
meet face-to-face or communicate through email. They found
Frontiers in Robotics and AI | www.frontiersin.org 5November 2020 | Volume 7 | Article 586263
Nørskov et al. Robot-Mediated Interview and Fairness
that participants in the computer-mediated condition tried
to overcome the limitations of such communication format
by engaging in more personalized interactions. As a result,
the participants got to know each other more efficiently
with fewer message exchanges. This finding suggests that the
lack of non-verbal cues in computer-mediated communication
leads to a more intimate exchange compared to a face-to-
face situation (Tidwell and Walther, 2002). Similarly, Joinson
(2001) found that participants communicating through chat
showed higher levels of spontaneous self-disclosure compared
to those communicating face-to-face. Visually anonymous
participants also disclosed more personal information than
non-visually anonymous participants did. In both studies, the
hyperpersonal model (Walther, 1996) was suggested as a possible
explanation. Because feedback cues, and in particularly visual
cues, are minimal in human-computer interaction, there is less
information available to confirm or contradict our expectations.
As a result, people choose to focus on cues that confirm their
expectations, and may even adapt their behavior in order that
make expectation confirmation more likely (Walther, 1996).
So, although computer-mediated communication conveys less
information about a conversation partner, it seems that this
anonymity facilitates a more personal communication with
a higher level of self-disclosure (Joinson, 2001; Tidwell and
Walther, 2002).
With the aim to maintain anonymity while transmitting
some of the facial information relevant for communication,
Wang et al. (2014) examined the instant-messaging tool
KinChat, which adds facial expression and head movement
to text-based communication without revealing the user’s face.
The authors reported that the addition of facial expressions
and head movement enhanced the level of understanding in
communication (Wang et al., 2014). Robot-mediated interviews
may therefore hold potential for promoting the information
exchange in the job interview as well as reduce the effect of visual
cues (e.g., gender) on the interviewers’ way of communicating,
thus limiting implicit biases.
Research on physically embodied robots showed that
participants preferred interacting with an embodied robot than
with a virtual one (Lee et al., 2006). Embodiment yields a
greater sense of social presence, which was found to mediate
the evaluation of the interaction. Wang and Rau (2019) also
tested the effect of embodiment and found that embodied robots
were preferred over other kinds of virtual reality, augmented
reality, and telepresent robots. Kiesler et al. (2008) had similar
findings. Their study compared an embodied humanoid robot
and an on-screen robot. Both robots interviewed people about
their health. Participants liked the embodied robot more, and
attributed stronger and more positive personality traits to it,
but they were more inhibited in their interaction. For instance,
they would make fewer disclosures about socially undesirable
behavior. The authors argued that the participants behaved as if
they perceived the embodied robot as a human, and that this was
not the case when communicating with the on-screen robot.
Papadopoulos et al. (2012) added a robot to a communication
system in a collaborative computer game to enhance the
remote communication of players. The presence of a robot
increased the number of social cues (smiles and non-task-related
speech) expressed by participants. Kim et al. (2009) investigated
communication constraints in a human-robot interaction
compared to human-human interaction. They found that
human-human communication involved more social constraints
(feelings, non-imposition, and disapproval), while task-oriented
constraints (clarity and effectiveness) were applied equally for
robot and human. Tanaka et al. (2014) compared video, avatar,
and robot-mediated communication. They found that having
a robot that was a physical embodiment of a communicator
enhanced the social telepresence compared to video and audio-
only conferencing. Research thus indicates that physically
present robots may hold advantages for technology-mediated
job interviews compared with on-screen robots, telephone, chat,
etc. The main reason being that the feeling of social presence is
greater, which has a positive impact on communication.
Indeed, Edwards et al. (2016) and Edwards et al. (2019)
conducted a series of studies that investigated the expectations
that participants have before interacting with a robot and
how these change during and after their interaction. They
documented that people anticipated less uncertainty, more liking,
and more social presence when expecting to interact with a
human compared to a robot. However, after a single brief
interaction with a social robot, participants became less uncertain
and felt more social presence than expected (Edwards et al.,
2019). On the other hand, participants who had a brief interaction
with another human seemed to lower their ratings of social
presence after the interaction. The authors argued that the
existence of hyperpersonal effects (Walther, 1996) in human-
robot communication may explain the results. While face-to-
face interactions entail more visual cues, which can contradict
our expectations for the interaction and create less room for
confirmation, in human-robot interaction people tend to focus
on cues that confirm their expectations, which leads them to
develop greater affinity for the other.
Extant research thus suggests that the use of different
technologies in the employment interview has the potential to
alter and improve the circumstances under which applicants
are assessed by interviewers. Different technologies, among
these embodied teleoperated robots, offer new perspectives
on the employment interview, in particular by creating an
environment with lower levels of perceptual cues for bias,
which in turn promotes the information exchange in the job
interview, facilitates increased personal communication with
a higher level of self-disclosure, and involves fewer social
constraints than face-to-face communication—all of which may
contribute to increased fairness of applicant selection decisions.
We therefore hypothesize:
H2: The average fairness perceptions (i.e., procedural fairness
and interactional fairness) will be higher in a robot-mediated
job interview than in a face-to-face job interview.
Behavioral Intentions and Interview Setup
Applicant perceptions hold consequences for both organizations
and applicants. Positive or negative applicant perceptions
of the selection process may, for instance, affect applicants’
Frontiers in Robotics and AI | www.frontiersin.org 6November 2020 | Volume 7 | Article 586263
Nørskov et al. Robot-Mediated Interview and Fairness
work commitment, psychological well-being and performance,
if the applicant accepts the job (Gilliland, 1993; Schuler,
1993; McCarthy et al., 2017). In general, applicant fairness
perceptions are positively related to their behavioral intentions
(i.e., intentions of job acceptance, reapplication intentions, and
recommendation intentions) (McLarty and Whitman, 2016).
A meta-analysis of 86 independent samples documented that
applicants who had positive perceptions about the selection
process were more likely to perceive the organization in a
favorable light, and to exhibit stronger behavioral intentions
(Hausknecht et al., 2004). This means that the perception of the
selection process affects the hiring organization’s reputation and
its chances of attracting qualified workforce (Ryan and Huth,
2008). Thus, organizations are not the only party that is selecting.
Applicants also select, e.g., whether they will apply, where they
will apply, whether they will accept the job etc (Rynes, 1993).
So, the higher the applicant perceptions, the higher will be their
behavioral intentions will be (Nikolaou and Georgiou, 2018). We
thus hypothesize:
H3: The average behavioral intentions will be higher in
a robot-mediated job interview than in a face-to-face
job interview.
H4: Fairness perceptions and behavioral intentions will be
positively related.
Applicant Personality, Fairness
Perceptions, Behavioral Intentions and
Interview Setup
Fairness perceptions and behavioral intentions of the interview
may further vary depending on applicant personality.
Extraversion is a core personality dimension, which is related
to the social part of personality (Fishman et al., 2011). People
who score high on introversion are typically associated with
being reflective, shy and distant, while people who score high
on extraversion are active, sociable and dominant (Gudjonsson
et al., 2004). A study by Stritzke et al. (2004) found that shy
individuals differed from non-shy individuals in their judgement
of face-to-face situations, as measured by rejection sensitivity,
initiating relationships, and self-disclosure. However, shy and
non-shy individuals did not score differently on these measures
in computer-mediated communication situations. The authors
argued that the absence of visual and auditory cues in online
environments makes detecting negative or inhibitory feedback
cues from others less likely, and improves the communication
experience of shy individuals. Similarly, Hammick and Lee
(2014) found that shy individuals felt less communication
apprehension (i.e., fear or anxiety that something bad will
happen in the communication) in virtual reality compared to a
real-life face-to-face interaction. Robot-mediated interviews may
therefore be able to create a more comfortable situation for those
with introvert personality type.
Since extraverts process social stimuli differently than
introverts, and since they enjoy social interactions more than
introverts (Fishman et al., 2011), the two personality types may
thus prefer different types of job interviews, which is expected
to influence their fairness perceptions and behavioral intentions.
Research suggests that a favorability bias toward extraverts exists
in Western societies (Andersen and Klatzky, 1987; Paulhus and
Morgan, 1997; Swann and Rentfrow, 2001), which may put
introvert applicants at a disadvantage during job interviews.
Unlike extraverts, introverts are more likely to experience social
interactions as overstimulating and draining (Keirsey, 1998).
Removing some of the visual cues involved in job interviews
may reduce social stimuli to a degree that makes the situation
more comfortable for introvert applicants, thus allowing them to
perform better during a job interview. We therefore hypothesize
the following:
H5: Compared with extravert applicants, introvert applicants’
fairness perceptions will be higher in a robot-mediated
job interview.
H6: Compared with extravert applicants, introvert applicants’
behavioral intentions will be higher in a robot-mediated
job interview.
RESEARCH DESIGN
The present study was based on an online experimental
survey with video vignettes. A vignette can be defined as a
carefully prepared description of a person, object or situation,
representing a systematic combination of properties (Atzmüller
and Steiner, 2010, p. 128). Vignettes have been acknowledged for
being especially valuable in exploring perceptions, attitudes and
behaviors, and for not necessarily requiring participants to have
in-depth knowledge of the research topic in questions (Hughes,
2008, p. 918).
The use of experimental vignette method (EVM) in business
studies can be found in, for example, investigations of
accounting environments regarding ethical decision situations,
with accountants and accounting students as participants (Smith
and Rogers, 2000). EVM has also been applied to investigate
how the ways decision explanations are presented may influence
respondents’ (graduate students’) perceptions of trustworthiness
(Elsbach and Elofson, 2000). The use of EVM, however, is not
restricted to surveying students, but also to evidence real-life
managers’ ethical behaviors when their own economic well-
being may be at stake (Hoffman et al., 1998). In the study
of discrimination in job interviews, “fictitious” setups (such as
experimental vignettes) that use students as participants have also
surfaced (e.g., Kutcher and Bragger, 2004; Krings and Olivares,
2007; Lindner et al., 2014; Gioaba and Krings, 2017).
Notwithstanding, as emphasized by Aguinis and Bradley
(2014), EVM need not be limited to a written format. Rather,
EVM can include images, videos and other media. More
recently, the application of scripted video vignettes has emerged.
Such vignettes represent short, visual depictions of pre-written
(hypothetical) events (Hillen et al., 2013, p. 296). This vignette
format has increased realism (Burt et al., 2016) and engagement
(Davies et al., 2016). Nonetheless, this kind of EVM is still
a methodology in progress and as cautioned by Hillen et al.
(2013) from their review on applying scripted video vignettes
for experimental physician-patient communication research:
“No “gold standard” exists for most methodological issues
Frontiers in Robotics and AI | www.frontiersin.org 7November 2020 | Volume 7 | Article 586263
Nørskov et al. Robot-Mediated Interview and Fairness
encountered when conducting this type of research, as literature
testing the consequences of different approaches is lacking”
(p. 308).
However, considering the mentioned advantages of video
vignettes, we conducted an online experimental video-vignette-
based survey, which employed a 2 (personality type, introvert
or extravert) by 2 (type of job interview, face-to-face or robot-
mediated) experimental survey design. As the technology is
relatively new in this context, we found it critical to ensure
that the context was clearly presented in the survey. To ensure
that the respondents all had the same understanding of the two
job interview situations (robot-mediated vs. face-to-face) before
responding to the survey questions, the survey included a link to a
scripted video displaying a job applicant attending a job interview
(with and without a fair proxy involved). Since the study
examines four different conditions (2 ×2 design), each condition
was shown in a separate video. Survey respondents were
randomly assigned to two of the four conditions. Randomization
was undertaken automatically by the Qualtrics survey software.
Method
Participants
Study 1 was designed to test H1–H3. In this study, we conducted
a web-based experimental survey among bachelor students of
business administration at a Danish university. Respondents
were contacted through the University mailing list. We received
235 valid responses to the survey. Student respondents were
selected because we expected these respondents to possess
specific characteristics. First, we expected this age group to
be more used to adopting new technologies. Second, given
their limited work experiences and future high-in-demand
attractiveness in the present war-for-talent business situation,
this group is less likely to expect to be discriminated against in
the selection process.
Procedures
The experimental survey was delivered through the online
survey platform Qualtrics. On the first page of the survey
the respondents were informed of the overall purpose of the
study and were asked to give their consent to participate. A
total of 279 respondents took part in the survey, whereof 44
were excluded due to incomplete data (≥50% missing items)
leaving 235 valid responses. Hereof, 109 (67 male, mean age
22.8 years) respondents were randomly assigned to watch
two videos (a face-to-face job interview and a robot-mediated
job interview), in which the applicant was an introvert. The
remaining 126 (81 male, 45 female, mean age 22.9 years)
respondents were randomly assigned to watch two videos (a face-
to-face job interview and a robot-mediated job interview), in
which the applicant was an extravert. The videos were shown in
a random order whereby some respondents were first exposed
to the face-to-face interview and others to the robot-mediated
interview. Before and after each exposure to the video stimuli
the respondents were asked to fill in questionnaires (see below).
The videos were introduced with the following introduction to
the videos:
“In the following, you will see two short video segments
that we ask you to rate on different parameters. Both videos
portray an interview between Anna and Mark. Anna is a
job applicant, and Mark is a recruiter for an international
company. Anna is a young product designer who graduated
from the School of Design in Copenhagen 4 years ago. Anna
was employed immediately after graduation and worked for the
company “D.S. Design” in Copenhagen for nearly 4 years. She
gained increasing responsibility and was the lead designer on
several products. Anna loved the creative environment in D.S.
Design but there were not many opportunities for advancement.
Furthermore, 4 months ago she had to move to Aarhus for
personal reasons. She has been unemployed ever since, but has
now applied for a position in Alpha Designs, an international
design company based in Aarhus. Alpha Designs has invited
Anna for an interview, since her profile is a perfect match to the
position they seek to fill. The two short videos presented in this
survey show Anna being interviewed by Mark, a recruiter from
Alpha Designs. The two videos show two different job interview
situations. One is a face-to-face job interview, while the other one
is robot-mediated.”
In this study, we sought to uncover how respondents react
to the video vignette scenarios from a first-person and a third-
person perspective. We did this for the following reasons.
Firstly, the third-person perspective on vignette scenarios may
reduce the social desirability of responses (Wason et al., 2002;
Hughes and Huby, 2004) and desensitize possibly sensitive or
controversial topics (Finch, 1987). Social robots have indeed been
shown to pose significant ethical and moral challenges (Van
Wynsberghe, 2013; Vallor, 2015). In addition, social robots in job
interviews are not yet a part of a personnel selection practices
in organizations. As such, the investigated concept of the robot-
mediated interview is novel and may be perceived as unusual or
even eccentric. The use of vignettes, where participants cannot
be expected to have previous experiences with the technology,
can therefore help provide focus for the participants and clarify
the principles under study (Hughes, 2008, p. 920). It has been
argued that the paradoxical blend of animate and inanimate
features of social robotic agents escalates the novelty experience
during interactions with such agents (Smedegaard, 2019). All
of this may potentially make the robot-mediated interview a
sensitive concept, which is why combining a third-person and a
first-person perspective is necessary.
Secondly, research using vignettes has indeed documented a
discrepancy between what respondents think should happen and
what they do themselves (Carlson, 1996). We therefore made use
of both a first-person and a third-person perspective to tap into
the two dependent variables: perceptions of applicant fairness
and behavioral intentions. More specifically, applicant fairness
perceptions were assessed from a third-person perspective, and
behavioral intentions from a first-person perspective. In the case
of the latter, the respondents were asked to respond, as if they
were the applicant in the video (e.g., “If I were the applicant
in the video, I would accept the job if it was offered to me”).
Because it is well-established in the personnel selection literature
that applicant fairness perceptions and behavioral intentions
are positively related (e.g., McLarty and Whitman, 2016),
Frontiers in Robotics and AI | www.frontiersin.org 8November 2020 | Volume 7 | Article 586263
Nørskov et al. Robot-Mediated Interview and Fairness
TABLE 1 | Items used for measuring the three constructs.
Item Wording
Procedural fairness item 1 I think that using this interview process was a
neutral and unbiased way to select people for
the job of product designer.
Procedural fairness item 2 I think that the interview questions themselves
were fair.
Procedural fairness item 3 Overall, the method of interviewing used was
fair.
Interactional fairness item 1 Has the interviewer treated the applicant in a
polite manner?
Interactional fairness item 2 Has the interviewer treated the applicant with
dignity?
Interactional fairness item 3 Has the interviewer treated the applicant with
respect?
Interactional fairness item 4 Has the interviewer refrained from improper
remarks or comments?
Behavioral intentions item 1 I would accept the job if it was offered to me.
Behavioral intentions item 2 I would apply to this organization again.
Behavioral intentions item 3 Based on my experience with this interview
process, I would encourage others to apply for
employment with this organization.
investigating the two variables from two different perspectives
was expected to detect any differences and/or contradictions in
the respondents’ reactions to vignette scenarios, and increase the
validity of the results. So, if fairness perceptions and behavioral
intentions are positively related in the robot-mediated condition,
even though they are assessed from two different perspectives,
this may indicate that the responses given did not involve social
desirability effects and/or that the vignette scenarios were not
perceived as a sensitive topic.
Finally, at the end of the survey, we also included an open-
ended question to capture possible explanations of our findings
and reveal socially situated aspects of respondents’ responses.
Measures
We used well-validated scales for each of the constructs.
Procedural and interactional fairness were measured using Bauer
et al. (2001) scale. To assess behavioral intentions, we relied on
McLarty and Whitman (2016) scale. Table 1 includes the wording
for all items. The goal of the analysis is to determine the existence
of differences in the means for the three constructs based on the
experimental condition. From the description of the experiment,
this is a classical 2 ×2 within-between design.
Video Stimuli
The four scripted videos, each lasting ∼40 s, and each
representing the four conditions, displayed a female job applicant
attending a job interview (with and without a fair proxy
involved). The applicant was interviewed by a male interviewee,
and the interview dialogue was identical in all four videos.
Figure 2 shows the image from the video that included a fair
proxy, while Figure 3 shows the face-to-face job interview. In
the robot-mediated setup in Figure 2, the applicant and the
interviewer are seated in two different rooms, each of them sitting
with the robotic proxy representing the other party. Each robot is
teleoperated by the party it represents, and neither party can see
the other. In the videos showing a face-to-face job interview, the
applicant and the interviewee were seated in the same room.
The Robot
We used a teleoperated android robot, Telenoid R1, developed
by the Japanese robotics lab ATR Hiroshi Ishiguro Laboratories.
In the videos, the Telenoid mimicked the operator’s head
movements, emulated the operator’s lip movements, and
transmitted the operator’s speech. Visually, it is designed
to display a minimal human embodiment (Ishiguro, 2016),
appearing “both old and young” and “both male and female”
(Seibt and Vestergaard, 2018, p. 9). Extant studies have suggested
that the robot is perceived as lacking social identities (e.g., age,
gender) and visual cues, which made it easier for people to
focus on the conversation (Seibt and Vestergaard, 2018). For
these reasons, the Telenoid was selected to be studied in the job
interview setting.
Analysis
To examine the extent to which differences in means between the
various experimental conditions exist, we use SMM (Structured
Mean Model) analysis presented in Breitsohl (2019). This
analytical approach is also known as MACS (Mean and
Covariance Structures) analysis (see Bagozzi, 1977; Cole et al.,
1993; Ployhart and Oswald, 2004) and is closely related to
the seminal paper by Meredith (1993) addressing measurement
invariance. Among its key merits compared to traditional
ANOVA analysis are the ability to consider measurement error
(as opposed to simply calculating an average for a specific scale)
as well as handling missing values in an integrated way (although
missing values are not a problem for our sample). Disregarding
measurement errors may lead to an attenuation in sensitivity
to detect specific population mean differences as described by
Cohen (1988, p. 536).
We follow a sequential process in carrying out SMM as
suggested in Byrne (2012). This involves estimating, assessing
and testing a sequence of increasingly restricted confirmatory
factor analysis (CFA) models. In line with the existing literature
concerning CFA models, we use CFI (Bentler, 1990) and TLI
(Tucker and Lewis, 1973) as measures of goodness-of-fit with
a threshold of around 0.95 signifying well-fitting models for
both. We also assess Root Mean Square Error of Approximation
(RMSEA) suggested by Steiger and Lind (1980) (cf. Steiger,
2016) with values below 0.05 signifying good fit, values between
0.05 and 0.08 signifying reasonable error of approximation, and
values between 0.08 and 0.1 associated with mediocre fit. Finally,
Standardized Root Mean Square Residual (SRMR) values <0.08
signify good fit. Each of the three scales employs a five-point
Likert scale. To accommodate obvious deviations from normality
we used the MLM estimator, which is a robust alternative to
the usual ML estimator associated with confirmatory factor
analysis. Hence, to assess the validity of the increasingly restricted
sequence of models we use the scaled difference chi-square
test (1MLM) (Satorra and Bentler, 2001). All analyses are
carried out using Mplus 7.11 (Muthén and Muthén, 2012).
Frontiers in Robotics and AI | www.frontiersin.org 9November 2020 | Volume 7 | Article 586263
Nørskov et al. Robot-Mediated Interview and Fairness
FIGURE 3 | A scene from the face-to-face job interview that was used in the survey.
Figure 4 provides an overview of the SMM framework. Steps
1a-1d in the abovementioned sequence entail establishing well-
fitting baseline models. We do this by estimating for each of the
four conditions and scrutinizing the adequacy of each model.
Step 2 is to estimate a well-fitting configural model. In this
setup, the configural model equals the model obtained by jointly
estimating all four baseline models. Given that the baseline
models in Steps 1a-1d are well-fitting the configural model will
typically also be well-fitting.
Step 3 addresses the question of equal loadings of the CFA
model across the four experimental conditions. We carry out this
assessment using the configural model from step 2 as the point of
departure. In an iterative procedure, we assess whether equality
restrictions regarding the loadings across the four conditions
for every construct are warranted using the scaled difference
chi-square test. In a similar vein, we assess whether equality
restrictions regarding the indicator intercepts are justifiable in
step 4. Thus, having established partial measurement invariance,
we can finally test for differences across the four groups in terms
of means of the latent variables. In step 5, we set the basis for
the analyses of the structural part by estimating a model with
the same set of restrictions regarding the measurement part
as was the result of step 4 and no equality restrictions at all
regarding the latent means across the four conditions. Step 6
imposes restrictions regarding the latent means corresponding to
no interaction effects. Finally, step 7, based on a non-significant
increase in the scaled chi-square test between step 5 and step 6,
assesses the viability of restrictions corresponding to no main
effects. The models from step 5 to 7 are used to test all
our hypotheses. We analyze differences in latent means based
on a structural model where all three constructs are allowed
to correlate as well as a structural model, where behavioral
intentions is the dependent variable, and procedural fairness and
behavioral fairness act as covariates.
Results
As suggested by Brown (2015), in situations where the indicators
are expected to be correlated due to temporal dependence (the
same construct being measured on two different occasions) it is
common to permit for this kind of dependence by allowing the
error terms in a CFA model to be correlated. However, when
we allowed for temporal dependence (the within-subject part of
the design) in a preliminary analysis, none of the correlations
between the error terms were significant. This may not be too
surprising given that we do not have a genuine longitudinal
design, but merely a situation where the same subjects provide
answers to two different situations (robot-mediated vs. face-
to-face). Thus, allowing for correlation between the constructs
driving the manifest indicators leaves no residual correlation
between those indicators’ error terms. Hence, we analyze the data
from this experiment as a 2 ×2 between-subjects design. This
Frontiers in Robotics and AI | www.frontiersin.org 10 November 2020 | Volume 7 | Article 586263
Nørskov et al. Robot-Mediated Interview and Fairness
TABLE 2 | Results of SMM analysis.
RMSEA CFI TLI SRMR d.f. 1MLM
Baseline: Face-to-face-introvert 0.000 1.000 1.003 0.044 32
Baseline: Face-to-face-extravert 0.063 0.969 0.957 0.049 32
Baseline: Robot-mediated-introvert 0.088 0.939 0.915 0.071 32
Baseline: Robot-mediated-extravert 0.061 0.966 0.953 0.070 32
Configural model 0.054 0.979 0.970 0.060 128
Partial equal loadings model 0.053 0.975 0.971 0.120 155 33.96
Partial equal loadings and intercepts model 0.073 0.947 0.947 0.153 182 98.79**
No restrictions on latent means 0.060 0.967 0.964 0.071 164
No interaction effects for any of the latent means 0.059 0.967 0.965 0.071 167 1.188
No interactions and no effect of personality for any of the latent means 0.058 0.968 0.966 0.073 170 1.898
*p-value <0.05, **p-value <0.01. The 1MLM statistic tests the model in the current line against the model in the previous line.
TABLE 3 | Parameter estimates of latent means.
Mean of
procedural
fairness
Mean of
interactional
fairness
Mean of
behavioral
intentions
Face-to-face-introvert
(reference group)
0 (0) 0 (0) 0 (0)
Robot-mediated-
introvert
−0.398 (0.219) −0.443 (0.141) −0.831 (0.206)
Face-to-face-extravert 0.077 (0.138) 0.049 (0.121) 0.226 (0.135)
Robot-mediated-
extravert
−0.325 (0.203) −0.405 (0.144) −0.823 (0.201)
Standardized estimates (standard error). Initial latent model.
is also in accordance with the recommendations in Ployhart and
Oswald (2004).
The first step established baseline models for each of the
four experimental conditions (face-to-face introvert, face-to-
face extravert, robot-mediated introvert, and robot-mediated
extravert). Table 2 shows that all baseline models satisfy the
requirements outlined in the methods section. A possible
exception might be RMSEA for robot-mediated-introvert.
However, the RMSEA is just above the threshold for mediocre fit
and the remaining goodness-of-indices all point at a well-fitting
baseline model.
Next followed the estimation of a configural model, which is
a single model comprising all four baseline models without any
limitations on the estimated parameters (except for those relevant
for identification). As this corresponds to the simultaneous
estimation of the four baseline models, it can be established that
the configural model is a well-fitting model.
Third, we addressed the question of equal factor loadings.
The model reported in Table 2 is an equal factor-loading
model, where all factor loadings are equal except for item 2
for procedural fairness, which can operate freely across the
four conditions.
During the fourth step, we imposed restrictions in terms
of equal indicator means. The model reported in Table 2 has
equal indicator means for all indicators except for item 3 for
procedural fairness, item 2 for behavioral intentions, and item
3 for behavioral intentions. For the latter three indicators the
means are constrained to be equal for robot-mediated extravert
and robot-mediated introvert combinations as well as for the
face-to-face introvert and face-to-face extravert combinations.
Although the necessary restrictions are not justifiable from a
statistical point of view (p-value <0.001), the final model does
constitute a well-fitting model based on the criteria outlined.
This allowed us to examine the structural part of the CFA
model, which is the means and variances of the latent variables.
Of particular relevance for this explorative study is the extent
to which the latent means differ across the four experimental
conditions. Table 3 holds the estimated means of the latent
variables for the four conditions in the model without any
restrictions on the latent means (step 5 in the SMM procedure).
We move ahead in a stepwise fashion as recommended in the
literature (see e.g., Kirk, 1995). Based on the non-significant
scaled chi-square test between the models from step 5 and step
6, our first intermediate result states that there are no interaction
effects of personality of applicant and type of interview for any of
the means of the three latent variables. Thus, we cannot reject
H1 (see Table 2). Furthermore, based on the non-significant
scaled chi-square test between the models from step 6 and
step 7, H5 and H6 are rejected, i.e., there is no effect of the
personality of the applicant for any of the latent means. Table 4
holds the standardized estimated parameter estimates of the
difference in means between the face-to-face and the robot-
mediated interviews for each of the three latent variables. Based
on the results in Table 4, we reject H2 and H3, i.e., the means
of the latent variables depend on the type of interview but the
direction is opposite to what we expected. Table 5 shows the
parameter estimates for the latent means in a model where
behavioral intentions are driven by the two fairness constructs.
Interestingly, the parameter estimate for the mean of behavioral
intentions is somewhat smaller compared to the unrestricted
model (−0.488 compared to −0.711). However, the parameter
estimate is still statistically significant. Thus, even when we
control for the differences in means between the face-to-face and
the robot-mediated interviews for fairness perceptions, there is
still a difference between the means for behavioral intentions.
Frontiers in Robotics and AI | www.frontiersin.org 12 November 2020 | Volume 7 | Article 586263
Nørskov et al. Robot-Mediated Interview and Fairness
TABLE 4 | Parameter estimates of latent means.
Mean of
procedural
fairness
Mean of
interactional
fairness
Mean of
behavioral
intentions
Face-to-face (reference
group)
0 (0) 0 (0) 0 (0)
Robot-mediated −0.287 (0.117) −0.438 (0.098) −0.711 (0.115)
Standardized estimates (standard error). Final latent model.
TABLE 5 | Parameter estimates of latent means.
Mean of
procedural
fairness
Mean of
interactional
fairness
Mean of
behavioral
intentions
Face-to-face (reference
group)
0 (0) 0 (0) 0 (0)
Robot-mediated −0.293 (0.119) −0.445 (0.098) −0.488 (0.108)
Standardized estimates (standard error). Controlling for covariates.
TABLE 6 | Parameter estimates of the relationship between behavioral intentions
and fairness.
Behavioral intentions
Robot-mediated Face-to-face
Procedural fairness 0.326(0.085) 0.400 (0.078)
Interactional fairness 0.442 (0.080) 0.223 (0.061)
Standardized estimates (standard error).
Finally, Table 6 shows the associated slope coefficients from the
model reported in Table 5. We have a positive and significant
relationship between behavioral intentions and procedural as well
as interactional fairness, thus confirming H4.
Finally, since only 11 respondents provided answers to the
open-ended question, it did not justify an elaborate analysis of
these data. Nonetheless, we categorized the answers into themes
that indicate some potential explanations for our survey findings.
The answers were related to: (i) the perceptions of and attitudes
toward the robot-mediated job interview, and (ii) perceptions
of the robot’s physical appearance. The answers did not reflect
a clear-cut attitude toward the concept of robots as proxies in
job interviews, but pointed out both positive and negative aspects
of the concept. Some expressed a general, positive perception of
the concept, i.e., “wonderful robotic interview”, “I find this robot
really impressive idea,” “the idea behind this technology [. . . ]
is brilliant.” Others had negative perceptions, and in particular
pointed out three interrelated aspects, which may get lost in
the robot-mediated job interview: emotions that do not get
transmitted due to the robot’s design, the loss of intimacy between
the interviewer and the applicant, and the lack of opportunity
to feel the chemistry between the two parties (e.g., “I will always
prefer a face-to-face interview, so they can get a feel of me
and I can get a feel of them”). Nonetheless, one respondent
remarked that “the technology works fine if it is impossible
to do the interview in person,” indicating that the robot-
mediated interview could be appropriate in certain situations,
but should not replace the face-to-face interview completely.
Finally, the appearance of the robot was perceived negatively by
some, e.g., “[it is] somehow in a crucified posture,” and “[the
interview becomes] bizarre because of the doll.” While we cannot
generalize based on the few responses generated by the open-
ended question, these responses may indicate some of the reasons
for our finding that the face-to-face interview is perceived more
fairly, namely the robot’s physical appearance and the affective
dimensions of interpersonal communication that are removed
from the robot-mediate setup. The responses also indicate that
the concept in itself has potential, but that another type of robot
could be more suitable.
DISCUSSION
This paper addresses a novel area of inquiry, namely the
robot-mediated job interview. The paper makes two main
contributions. Firstly, it examines the case of a symmetrical visual
anonymity in a job interview as opposed to the asymmetrical
visual anonymity that prior research has conceptualized
(Seibt and Vestergaard, 2018). Secondly, it contributes to the
understanding of when the robot-mediated job interview may be
a suitable alternative to the face-to-face job interview.
Our main finding showed that face-to-face employment
interviews were perceived as fairer. This was the case both
in the condition involving an introvert applicant and in the
condition involving an extravert applicant. This lack of effect of
applicant personality is somewhat surprising, as extant research
has documented positive effects of impression management in
job interviews, thus favoring extravert applicants. The perception
that face-to-face interview is fairer is also unexpected considering
that face-to-face communication is particularly conducive to
discrimination (Rivera, 2012). We further found that applicants’
fairness perceptions positively affect their behavioral intentions.
The intentions to accept the job (if offered one), reapply to the
organization, and recommend it to other jobseekers were thus
higher in the face-to-face setup than in the robot-mediated setup.
However, when controlling for fairness perception of the two
setups, the difference in behavioral intentions between the setups
was still present and significant, albeit the effect was not as strong.
This finding indicates that other factors than fairness perceptions
need to be considered in order to fully understand why behavioral
intentions were more positive in the face-to-face job interview.
Prior research suggests that behavioral intentions are shaped
by applicants’ dispositional factors, such as Big Five personality
dimensions, cognitive ability (Merkulova et al., 2014) and core
self-evaluations (McLarty and Whitman, 2016), because people
with higher self-beliefs about their abilities to perform well are
more likely to form stronger behavioral intentions (Ajzen, 2011).
Perceptions and behavioral outcomes are also shaped by factors
such as job-relatedness of the selection procedure (Gilliland,
1993) and evaluations of the interviewer (Sears et al., 2013).
Further research should examine how and why such personal and
situational factors influence applicants’ perceptions of the robot-
mediated interview in order to get a better grasp of the benefits
and shortcomings of this novel type of interview setup.
Frontiers in Robotics and AI | www.frontiersin.org 13 November 2020 | Volume 7 | Article 586263
Nørskov et al. Robot-Mediated Interview and Fairness
Implications for Research
The study points toward new research questions to be answered.
More specifically, the finding that the robot-mediated job
interview was perceived as less fair calls for additional reflections.
We tentatively suggest the following possible explanations. First,
the relevant target group for the robot-mediated job interview
may be a particular segment of applicants. Applicant perceptions
may, for instance, depend on whether they have experienced
discrimination. Applicants that have previously experienced
discrimination during selection and recruitment processes may
perceive the robot-mediated job interview differently (i.e., more
positively) than those (with limited job search and work
experience) who have felt justly treated and who have the
expectations of being “in high demand” on the job market, such
as our respondents.
Second, the degree of novelty of the technology and its use
in a new context (job interview) may have had an effect on
the resulting fairness perceptions. First impressions of a robot
are likely to be biased, but they are expected to change during
interactions with the robot (Dautenhahn, 2007). Indeed, research
suggests that it takes a couple of minutes for subjects to get used
to the Telenoid and the novel situation in which the robot is used
(Seibt and Vestergaard, 2018). However, our online vignette-
based experimental survey only involved very brief video stimuli
rather than interaction or engagement with the robot. The
respondents’ first impressions of the robot therefore did not have
a chance to settle and thus remained unchallenged (Smedegaard,
2019).
Third, the finding also prompts considerations related to the
design of a robotic fair proxy. While a number of visual cues
(e.g., from body gestures to facial expressions, from gender to
race) can trigger negative assessments, it is possible that removing
only a few specific rather than the entire range of cues would be
perceived more positively by applicants. Furthermore, different
segments of applicants may prefer to remove different visual
cues. The insights from the open-ended question in our survey
also indicate that the design of the Telenoid, which was used in
the study, may be perceived as unusual and even creepy and/or
distresing. While it could be argued that the “minimal design” of
the Telenoid may hold advantages for reducing implicit biases,
some respondents perceived it as “a doll” and “bizarre,” thus
indicating a potential uncanny valley effect (Mori et al., 2012).
Four, the robot-mediated job interview may be perceived
as less fair because it places a technology in between the
interviewer and the applicant. Such “intervention” may be
viewed as a way to control the selection process rather
than make it fairer. In addition, compared with face-to-face
communication, technology-mediated communication has been
shown to increase the degree to which people open up and engage
in spontaneous self-disclosure (Joinson, 2001). Similarly, this
may elicit negative perceptions of the robot-mediated interview,
and be viewed as a way of manipulating the applicant to
open up more than she would normally, thus reducing fairness
perceptions of the robot-mediated job interview. Future research
could examine whether a different framing of the robot-mediated
interview situation would lead to different results.
Five, inexperienced jobseekers may perceive (more) useful
experiences from engaging in face-to-face communication in
their job search. Experiencing the face-to-face interview will have
the advantage of learning how interviewers react verbally and
non-verbally to different self-presentation and communication
strategies of job applicants, which would promote the applicants’
learning about how to perform well during job interviews. This
experience may be the necessary foundation before an applicant
is able to reap the benefits of the robot-mediated job interview.
Lastly, the lower fairness perceptions of the investigated robot-
mediated interview may be due to the symmetrical nature of
the FPC in our study. While the symmetrical version of the
FPC is intended to make the job interview fairer for both
interviewers and applicants, it may be perceived as having
too great a psychological distance (Trope and Liberman, 2010)
between the communicating parties. A job interview is a
high-stake context in which applicants want to make a good
impression, which demands a certain degree of self-promotion
(Schreurs et al., 2018). This degree of self-promotion can be
more easily adjusted if the applicant is able to see the non-
verbal reactions of the interviewer, which is possible in the
asymmetrical version of the FPC. However, in the symmetrical
version of the FPC, this is not the case. If applicants consider
self-promotion as being an important part of the job interview,
then removing the possibility to adjust the self-promotion tactics
to the reactions of the interviewer may have a negative effect on
their perceptions of the robot-mediated setup. Nonetheless, such
possibility for impression management may bias the judgement
of the interviewer (Howard and Ferris, 1996). Future research
could thus investigate the boundary conditions for the robot-
mediated job interview, in particular the relevant target group(s),
and how different target groups perceive the symmetrical and
asymmetrical versions of the robot-mediated job interview.
Implications for Practice
The study, has also some practical implications. The design of a
robotic agent for personnel selection is not only pertinent because
of the way the robot itself may be perceived by applicants, but also
because the robot’s design is likely to have consequences for the
way the organization is perceived. This is because the robot is,
in a way, an organizational representative (Nørskov and Ulhøi,
2020), and thus may influence the applicant’s perceptions of the
organizational attraction (Turban and Dougherty, 1992). This
may in turn have consequences for organizational reputation
(McCarthy et al., 2017) and its ability to attract applicants
(Ryan and Huth, 2008). Consequently, there is a need for
hiring organizations and robot designers to consider how the
kinetic, physical and functional features of a robot may promote
interaction between applicants and interviewers in desirable
ways. With respect to the design of the robot used in this study,
the responses to the open-ended question in the survey indicated
that the robot’s physical appearance may have been one of the
reasons why the robot-mediated interview was not perceived
more positively. On the one hand, this can be related to the
respondents’ limited exposure time to such a novel concept
and technology, as discussed above. On the other hand, it is
worthwhile considering whether the robot should be able to
transmit more of the facial mimicry and body movements.
Additional features of this type would, however, lead to some
Frontiers in Robotics and AI | www.frontiersin.org 14 November 2020 | Volume 7 | Article 586263
Nørskov et al. Robot-Mediated Interview and Fairness
trade-offs if the “minimalist” design of the Telenoid is changed.
Adding features that increase facial mimicry (e.g., smiling and
blinking) and body movement (e.g., leaning forward) may lead
to even more social presence, and to getting a “better feel” of
the applicant. Indeed, recent research reveals that adding more
humanlike kinetic cues to a social robot increases the perceived
intimacy of the interaction (Xu, 2019). However, adding such
features will entail the disadvantage of allowing certain triggers
to perceptual biases, as applicants with more lively facial mimicry
and body movement are typically associated with being extravert,
which may lead to favorability bias and more positive assessments
(Paulhus and Morgan, 1997; Swann and Rentfrow, 2001). The
design of a robotic fair proxy in job interviews thus needs to be
able to strike a delicate balance between allowing enough social
cues into the interaction while at the same time reducing the
presence of those cues that are most likely to trigger implicit
biases and potentially lead to discrimination of applicants.
Such design also needs to be aligned with the image that the
hiring organization seeks to signal to its external stakeholders
regarding the type of workplace the organization is or aims
to be.
Limitations
Several limitations apply to this study. First, the use of business
students calls for some reservations. Despite the fact that such
populations have long been an accepted tradition in social
psychology and business studies, they do of course not reflect
a representative sample of job applicants and the associated
probabilities of experiencing discrimination. We chose students,
as they are “born digitals” and thus expected to be used to new
technologies. Given that our respondents are young, in high
demand after completion of their education, and have limited
experience with job interviews, their previous opportunities
for experiencing possible discrimination are likely to be low.
Second, as discussed above, the exposure time to the video-
based stimuli material in the experimental survey was relatively
short, around 40 s. There is a tradeoff, however, between adding
more time-consuming features to an online survey and securing
a satisfactory response rate. Allowing for longer exposure to a
novel technology in a setup that currently does not use such
technology may have offset the respondents’ first impressions and
conceptions of the robot-mediated job interview and changed
them. Third and relatedly, that the respondents were not
physically engaged in the interaction may have affected their
stance toward the robot. It has been found that the physical
presence of a robot affects the extent to which respondents
will rate a robot positively and which interactions they can
imagine engaging in with said robot (Bainbridge et al., 2011).
Finally, witnessing an interaction with a robot is likely a new
experience to the majority of respondents, which may predispose
them to react with surprise or indecision—reactions that may be
intensified when taking the third-person perspective (Kahn et al.,
2011; Turkle, 2011). The exact nature and impact of these issues
on the present study cannot be determined.
CONCLUSION
By relying on an online video vignette-based experimental
survey, this paper examined how the use of new technology
during employment interviews affects applicants’ fairness
perceptions. Using a robot as a fair proxy in the employment
interview is a novel approach for conducting interviews and
has as yet not been experienced by applicants. Our findings
show that the robot-mediated interview is perceived as less fair
than the face-to-face interview. Nonetheless, as limitations of
our study indirectly suggest, it would be important to test this
interview technique across different segments of applicants to
establish whether certain groups in the labor market who, for
instance, given their previous experiences with discrimination in
job interviews, and their socio-economic and/or socio-cultural
background may be more likely to express different perceptions
of the robot-mediated and face-to-face interviews.
DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will be
made available by the authors, without undue reservation.
ETHICS STATEMENT
Written informed consent was obtained from the individuals
for the publication of any potentially identifiable images or data
included in this article.
AUTHOR CONTRIBUTIONS
SN contributed to the conceptualization, research design, data
collection, and took the lead in writing the manuscript. MD
and JU were involved in conceptualization, research design,
data collection, and writing. MJ conducted the data analysis
and contributed to the writing of the manuscript. CE was
involved in the conceptualization and research design. JS
conceived the main idea for the study, and was involved in
the conceptualization. All authors contributed to the article and
approved the submitted version.
FUNDING
This work is supported by a Carlsberg Foundation Semper
Ardens Grant (F16-0004). Any opinions, findings, conclusions,
and/or recommendations expressed in this work are those of
the authors and do not necessarily reflect the views of either
the sponsor or the employer(s) of the authors. The usual
disclaimers apply.
REFERENCES
Adams, J. S. (1965). “Inequity in social exchange,” in Advances in Experimental
Social Psychology, Vol. 2, ed L. Berkowitz (New York, NY: Academic Press),
267–299. doi: 10.1016/S0065-2601(08)60108-2
Adrian, L., Druckman, D., Filzmoser, M., Damholdt, M. F., Koeszegi, S. T., Navarro
Guerrero, N., et al. (2019). “Fair proxy mediation - first experimental results
with tele-operated robots. Artificial intelligence in robotics,” in Paper Presented
at International Conference on Group Decision and Negotiation in 2019 a Joint
GDN-EWG/BOR Meeting (Loughborough).
Frontiers in Robotics and AI | www.frontiersin.org 15 November 2020 | Volume 7 | Article 586263
Nørskov et al. Robot-Mediated Interview and Fairness
Aguinis, H., and Bradley, K. J. (2014). Best practice recommendations for designing
and implementing experimental vignette methodology studies. Organ. Res.
Methods 17, 351–371. doi: 10.1177/1094428114547952
Ajzen, I. (2011). The theory of planned behavior: reactions and reflections. Psychol.
Health 26, 1113–1127. doi: 10.1080/08870446.2011.613995
Amodio, D. (2014). The neuroscience of prejudice and stereotyping. Nat. Rev.
Neurosci. 15, 670–682. doi: 10.1038/nrn3800
Andersen, S. M., and Klatzky, R. L. (1987). Traits and social stereotypes: levels
of categorization in person perception. J. Pers. Soc. Psychol. 53, 235–246.
doi: 10.1037/0022-3514.53.2.235
Atzmüller, C., and Steiner, P. M. (2010). Experimental vignette studies in survey
research. Methodology 6, 128–138. doi: 10.1027/1614-2241/a000014
Bagozzi, R. P. (1977). Structural equation models in experimental research. J. Mark.
Res. 14, 209–226. doi: 10.1177/002224377701400209
Bainbridge, W. A., Hart, J. W., Kim, E. S., and Scassellati, B. (2011). The benefits
of interactions with physically present robots over video-displayed agents. Int.
J. Soc. Rob. 3, 41–52. doi: 10.1007/s12369-010-0082-7
Bauer, T. N., Truxillo, D. M., Sanchez, R. J., Craig, J. M., Ferrara, P., and
Campion, M. A. (2001). Applicant reactions to selection: development of
the selection procedural justice scale (SPJS). Pers. Psychol. 54, 387–419.
doi: 10.1111/j.1744-6570.2001.tb00097.x
Bencharit, L. Z., Ho, Y. W., Fung, H., Yeung, D., Stephens, N., Romero-
Canyas, R., et al. (2018). Should job applicants be excited or calm? The role
of culture and ideal affect in employment settings. Emotion 19, 377–401.
doi: 10.1037/emo0000444
Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychol. Bull.
107, 238–246. doi: 10.1037/0033-2909.107.2.238
Breitsohl, H. (2019). Beyond ANOVA: an introduction to structural equation
models for experimental designs. Organ. Res. Methods 22, 649–677.
doi: 10.1177/1094428118754988
Brown, T. (2015). Confirmatory Factor Analysis for Applied Research, 2nd Edn. New
York, NY: Guilford Press.
Burt, J., Abel, G., Emmore, N., Lloyd, C., Benson, J., Sarson, L., et al. (2016).
Understanding negative feedback from South Asian patients: an experimental
vignette study. BJM Open 6, 1–8. doi: 10.1136/bmjopen-2016-011256
Buzzanell, P. M. (1999). Tensions and burdens in employment interviewing
processes: Perspectives of non-dominant group applicants. J. Bus. Commun. 36,
134–162. doi: 10.1177/002194369903600202
Byrne, B. M. (2012). Multivariate Applications Series. Structural Equation Modeling
With Mplus: Basic Concepts, Applications, and Programming. New York, NY:
Routledge/Taylor and Francis Group. doi: 10.4324/9780203807644
Byrne, Z. S., and Cropanzano, R. (2001). “The history of organizational justice: the
founders speak,” in Justice in the Workplace: From Theory to Practice, Vol.2, ed
R. Cropanzano (Mahwah, MJ: Lawrence Erlbaum Associates), 3–26.
Carlson, B. E. (1996). Dating violence: student beliefs about consequences. J.
Interpers. Violence 11, 3–18. doi: 10.1177/088626096011001001
Chapman, D. S., and Rowe, P. M. (2001). The impact of videoconference
technology, interview structure, and interviewer gender on interviewer
evaluations in the employment interview: a field experiment. J. Occup. Organ.
Psychol. 74, 279–298. doi: 10.1348/096317901167361
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences, 2nd Edn.
Hillsdale, NJ: Lawrence Erlbaum.
Cole, D. A., Maxwell, S. E., Arvey, R., and Salas, E. (1993). Multivariate
group comparisons of variable systems: MANOVA and structural equation
modeling. Psychol. Bull. 114, 174–184. doi: 10.1037/0033-2909.114.
1.174
Cuddy, A. J. C., Wilmuth, C. A., Yap, A. J., and Carney, D. R. (2015). Preparatory
power posing affects nonverbal presence and job interview performance. J.
Appl. Psychol. 100, 1286–1295. doi: 10.1037/a0038543
Dautenhahn, K. (2007). Socially intelligent robots: dimensions of human-
robot interaction. Philos. Trans. R. Soc. London B Biol. Sci. 362, 679–704.
doi: 10.1098/rstb.2006.2004
Davies, E. B., Wardlaw, J., Morriss, R., and Glazebrook, C. (2016). An experimental
study exploring the impact of vignette gender on the quality of University
students’ mental health first aid for peers with symptoms of depression. BMC
Public Health 16:187. doi: 10.1186/s12889-016-2887-2
Dobbin, F., and Kalev, A. (2016). Why diversity programs fail and what works
better. Harvard Business Review, p. 52–60.
Edwards, A., Edwards, C., Westerman, D., and Spence, P. R. (2019). Initial
expectations, interactions, and beyond with social robots. Comput. Human
Behav. 90, 308–314. doi: 10.1016/j.chb.2018.08.042
Edwards, C., Edwards, A., Spence, P. R., and Westerman, D. (2016). Initial
interaction expectations with robots: testing the human-to-human interaction
script. Commun. Stud. 67, 227–238. doi: 10.1080/10510974.2015.1121899
Elsbach, K. D., and Elofson, G. (2000). How the packaging of decision
explanations affects perceptions of trustworthiness. Acad. Manage. J. 43, 80–89.
doi: 10.2307/1556387
Finch, J. (1987). Research note: the vignette technique in survey research. Sociology
21, 105–114. doi: 10.1177/0038038587021001008
Fishman, I., Ng, R., and Bellugi, U. (2011). Do extraverts process
social stimuli differently from introverts? Cogn. Neurosci. 2, 67–73.
doi: 10.1080/17588928.2010.527434
Folger, R. (1977). Distributive and procedural justice: combined impact of “voice”
and improvement on experienced inequity. J. Pers. Soc. Psychol. 35, 108–119.
doi: 10.1037/0022-3514.35.2.108
Forscher, P. S., and Devine, P. G. (2015). “Controlling the influence of
stereotypes on one’s thoughts,” in Emerging Trends in the Social and Behavioral
Sciences: An Interdisciplinary, Searchable, and Linkable Resource, eds R. A.
Scott and M. C. Buchmann (New York, NY: John Wiley and Sons), 1–12.
doi: 10.1002/9781118900772.etrds0054
García, M. F., Posthuma, R. A., and Colella, A. (2008). Fit perceptions in the
employment interview: the role of similarity, liking, and expectations. J. Occup.
Organ. Psychol. 81, 173–189. doi: 10.1348/096317907X238708
Gilliland, S. W. (1993). The perceived fairness of selection systems: an
organizational justice perspective. Acad. Manage. Rev. 18, 694–734.
doi: 10.5465/amr.1993.9402210155
Gilliland, S. W., and Steiner, D. D. (1999). “Applicant reactions,” in The
Employment Interview Handbook, eds R. W. Eder and M. M. Harris (Thousand
Oaks, CA: SAGE Publications, Inc), 69–82. doi: 10.4135/9781452205519.n4
Gioaba, I., and Krings, F. (2017). Impression management in the job interview:
an effective way of mitigating discrimination against older applicants. Front.
Psychol. 8:770. doi: 10.3389/fpsyg.2017.00770
Grant, S., and Mizzy, T. (2014). Body weight bias in hiring decisions:
identifying explanatory mechanisms. Soc. Behav. Pers. 42, 353–370.
doi: 10.2224/sbp.2014.42.3.353
Graves, L., and Powell, G. (1996). Sex similarity, quality of the employment
interview, and recruiters’ evaluations of actual applicants. J. Occup. Organ.
Psychol. 69, 243–261. doi: 10.1111/j.2044-8325.1996.tb00613.x
Greenberg, J. (1987). A taxonomy of organizational justice theories. Acad. Manage.
Rev. 12, 9–22. doi: 10.5465/amr.1987.4306437
Guadagno, R. E., and Cialdini, R. B. (2007). Gender differences in impression
management in organizations: a qualitative review. Sex Roles 56, 483–494.
doi: 10.1007/s11199-007-9187-3
Gudjonsson, G. H., Sigurdsson, J. F., Bragason, O. O., Einarsson, E., and
Valdimarsdottir, E. B. (2004). Compliance andpersonality: the vulnerability
ofthe unstable introvert. Eur. J. Pers. 18, 435–443. doi: 10.1002/per.514
Hammick, J. K., and Lee, M. J. (2014). Do shy people feel less communication
apprehension online? The effects of virtual reality on the relationship between
personality characteristics and communication outcomes. Comput. Hum.
Behav. 33, 302–310. doi: 10.1016/j.chb.2013.01.046
Harmon, J., Schneer, J. A., and Hoffman, R. L. (1995). Electronic meetings
and established decision groups: audioconferencing effects on performance
and structural stability. Organ. Behav. Hum. Decis. Process 61, 138–147.
doi: 10.1006/obhd.1995.1011
Hausknecht, J. P., Day, D. V., and Thomas, S. C. (2004). Applicant reaction to
selection procedures: an updated model and meta-analyses. Pers. Psychol. 57,
639–683. doi: 10.1111/j.1744-6570.2004.00003.x
Hawkes, B., Cek, I., and Handler, C. (2018). “The gamification of employee
selection tools: an exploration of viability, utility, and future directions,” in
Educational and Psychological Testing in a Global Context. Next Generation
Technology-Enhanced Assessment: Global Perspectives on Occupational and
Workplace Testing, eds J. C. Scott, D. Bartram, and D. H. Reynolds
(Cambridge: Cambridge University Press), 288–313. doi: 10.1017/97813164075
47.013
Heilman, M. E., and Saruwatari, L. R. (1979). When beauty is beastly: the
effects of appearance and sex on evaluations of job applicants for managerial
Frontiers in Robotics and AI | www.frontiersin.org 16 November 2020 | Volume 7 | Article 586263
Nørskov et al. Robot-Mediated Interview and Fairness
and non-managerial jobs. Organ. Behav. Hum. Perform. 23, 360–372.
doi: 10.1016/0030-5073(79)90003-5
Hewlett, S. A., Marshall, M., and Sherbin, L. (2013). How diversity can drive
innovation. Harvard Business Review. Retrieved from: https://hbr.org/2013/12/
how-diversity- can-drive- innovation (accessed July 7, 2020).
Highhouse, S. (2008). Stubborn reliance on intuition and subjectivity
in employee selection. Ind. Organ. Psychol. 1, 333–342.
doi: 10.1111/j.1754-9434.2008.00058.x
Hillen, M. A., van Vliet, L. M., de Haes, H. C. J. M., and Smets, E. M. A.
(2013). Developing and administering scripted video vignettes for experimental
research of patient-provider communication. Patient Educ. Couns. 91, 295–309.
doi: 10.1016/j.pec.2013.01.020
Hinton, P. (2017). Implicit stereotypes and the predictive brain: cognition
and culture in “biased” person perception. Palgrave Commun. 3, 1–9.
doi: 10.1057/palcomms.2017.86
Hoffman, J. J., Couch, G., and Lamont, B. (1998). The effect of firm profit
versus personal economic well being on the level of ethical responses given by
managers. J. Bus. Ethics 17, 239–244. doi: 10.1023/A:1017936322433
Holgersson, C. (2013). Recruiting managing directors: doing homosociality.
Gender Work Organ. 20, 454–466. doi: 10.1111/j.1468-0432.2012.00595.x
Holroyd, J. (2012). Responsibility for implicit bias. J. Soc. Philos. 43, 274–306.
doi: 10.1111/j.1467-9833.2012.01565.x
Homan, A. C., van Knippenberg, D., Van Kleef, G. A., and De Dreu, C. K. W.
(2007). Bridging faultlines by valuing diversity: diversity beliefs, information
elaboration, and performance in diverse work groups. J. Appl. Psychol. 92,
1189–1199. doi: 10.1037/0021-9010.92.5.1189
Howard, J. L., and Ferris, G. R. (1996). The employment interview context: social
and situational influences on interviewer decisions. J. Appl. Soc. Psychol. 26,
112–136. doi: 10.1111/j.1559-1816.1996.tb01841.x
Huffcutt, A. I. (2011). An empirical review of the employment interview construct
literature. Int. J. Sel. Assess. 19, 62–81. doi: 10.1111/j.1468-2389.2010.00535.x
Hughes, R. (2008). “Vignettes,” in The Sage Encyclopedia of Qualitative Methods,
Vol. 2, ed L. M. Given (Los Angeles, CA: Sage), 918–920.
Hughes, R., and Huby, M. (2004). The construction and interpretation
of vignettes in social research. Soc. Work Soc. Sci. Rev. 11, 36–51.
doi: 10.1921/17466105.11.1.36
Ishiguro, H. (2016). “Transmitting human presence through portable
teleoperated androids: a minimal design approach,” in Human-Harmonized
Information Technology, Vol. 1, ed T. Nishida (Tokyo: Springer), 29–56.
doi: 10.1007/978-4-431-55867-5_2
Jeske, D., Shultz,. K. S., and Oweb, S. (2018). Perceived interviewee anxiety and
performance in telephone interviews. Evid. Based HRM Glob. Forum Empir.
Sch. 6, 320–332. doi: 10.1108/EBHRM-05-2018-0033
Johnson, S. K., Podratz, K. E., Dipboye, R. L., and Gibbons, E. (2010).
Physical attractiveness biases in ratings of employment suitability: tracking
down the “beauty is beastly” effect. J. Soc. Psychol. 150, 301–318.
doi: 10.1080/00224540903365414
Joinson, A. N. (2001). Self-disclosure in computer-mediated communication: the
role of self- awareness and visual anonymity. Eur. J. Soc. Psychol. 31, 177–192.
doi: 10.1002/ejsp.36
Kacmar, K. M., Delery, J. E., and Ferris, G. R. (1992). Differential effectiveness of
applicant impression management tactics on employment interview decisions.
J. Appl. Soc. Psychol. 22, 1250–1272. doi: 10.1111/j.1559-1816.1992.tb00949.x
Kahn, P. H., Jr., Reichert, A. L., Gary, H. E., Kanda, T., Ishiguro, H., Shen, S., et al.
(2011). “The new ontological category hypothesis in human-robot interaction,”
in Proceedings of the 6th ACM/IEEE International Conference on Human-Robot
Interaction (New York, NY: Association for Computing Machinery), 159–160.
doi: 10.1145/1957656.1957710
Keirsey, D. (1998). Please Understand Me II. Del Mar, CA: Prometheus Nemesis
Book Company.
Kiesler, S., Powers, A., Fussell, S. R., and Torrey, C. (2008). Anthropomorphic
interactions with a robot and robot–like agent. Soc. Cogn. 26, 169–181.
doi: 10.1521/soco.2008.26.2.169
Kim, M.-S., Sur, J., and Gong, L. (2009). Humans and humanoid social robots in
communication contexts. AI Soc. 24, 317–325. doi: 10.1007/s00146-009-0224-3
Kirk, R. (1995). Experimental Design: Procedures for the Behavioral Sciences. Pacific
Grove, CA: Brooks/Cole.
Kluger, A. N., and Rothstein, H. R. (1993). The influence of selection test
type on applicant reactions to employment testing. J. Bus. Psychol. 8, 3–25.
doi: 10.1007/BF02230391
Krings, F., and Olivares, J. (2007). At the doorstep to employment: discrimination
against immigrants as a function of applicant ethnicity, job type, and raters’
prejudice. Int. J. Psychol. 42, 406–417. doi: 10.1080/00207590701251721
Kutcher, E. J., and Bragger, J. D. (2004). Selection interviews of overweight job
applicants: can structure reduce the bias? J. Appl. Soc. Psychol. 34, 1993–2022.
doi: 10.1111/j.1559-1816.2004.tb02688.x
Lai, C. K., Skinner, A. L., Cooley, E., Murrar, S., Brauer, M., Devos, T., et al. (2016).
Reducing implicit racial preferences: II. Intervention effectiveness across time.
J. Exp. Psychol. Gen. 145, 1001–1016. doi: 10.1037/xge0000179
Landy, F. J. (2008). Stereotypes, bias, and personnel decisions: strange and stranger.
Ind. Organ. Psychol. 1, 379–392. doi: 10.1111/j.1754-9434.2008.00071.x
Langer, M., König, C. J., and Krause, K. (2017). Examining digital interviews
for personnel selection: applicant reactions and interviewer ratings. Int. J. Sel.
Assess. 25, 371–382. doi: 10.1111/ijsa.12191
Lee, K. M., Jung, Y., Kim, J., and Kim, S. R. (2006). Are physically
embodied social agents better than disembodied social agents?: the
effects of physical embodiment, tactile interaction, and people’s loneliness
in human–robot interaction. Int. J. Hum. Comput. Stud. 64, 962–973.
doi: 10.1016/j.ijhcs.2006.05.002
Li, J. (2015). The benefit of being physically present: a survey of experimental works
comparing copresent robots, telepresent robots and virtual agents. Int. J. Hum.
Comput. Stud. 77, 23–37. doi: 10.1016/j.ijhcs.2015.01.001
Lievens, F., De Corte, W., and Westerveld, L. (2015). Understanding the building
blocks of selection procedures: effects of response fidelity on performance and
validity. J. Manage. 41, 1604–1627. doi: 10.1177/0149206312463941
Lindner, N. M., Graser, A., and Nosek, B. A. (2014). Age-based hiring
discrimination as a function of equity norms and self-perceived objectivity.
PLoS ONE 9:e84752. doi: 10.1371/journal.pone.0084752
Macan, T. (2009). The employment interview: a review of current studies and
directions for future research. Hum. Resour. Manage. Rev. 19, 203–218.
doi: 10.1016/j.hrmr.2009.03.006
McCarthy, J., and Goffin, R. (2004). Measuring job interview anxiety:
beyond weak knees and sweaty palms. Pers. Psychol. 57, 607–637.
doi: 10.1111/j.1744-6570.2004.00002.x
McCarthy, J. M., Bauer, T. N., Truxillo, D. M., Anderson, N. R., Costa, A. C.,
and Ahmed, S. M. (2017). Applicant perspectives during selection: a review
addressing “So What?,” “What’s new?,” and “Where to next?”. J. Manage. 43,
1693–1725. doi: 10.1177/0149206316681846
McLarty, B. D., and Whitman, D. S. (2016). A dispositional approach to applicant
reactions: examining core self-evaluations, behavioral intentions, and fairness
perceptions. J. Bus. Psychol. 31, 141–153. doi: 10.1007/s10869-015-9405-x
McPherson, M., Smith-Lovin, L., and Cook, J. M. (2001). Birds of a
feather: homophily in social networks. Annu. Rev. Sociol. 27, 415–444.
doi: 10.1146/annurev.soc.27.1.415
Meredith, W. (1993). Measurement invariance, factor analysis and factorial
invariance. Psychometrika 58, 525–543. doi: 10.1007/BF02294825
Merkulova, N., Melchers, K. G., Kleinmann, M., Annen, H., and Tresch,
T. S. (2014). Effects of individual differences on applicant perceptions
of an operational assessment center. Int. J. Sel. Assess. 22, 355–370.
doi: 10.1111/ijsa.12083
Mori, M., MacDorman, K. F., and Kageki, N. (2012). The uncanny valley [from
the field]. IEEE Rob. Autom. Mag. 19, 98–100. doi: 10.1109/MRA.2012.21
92811
Muthén, L. K., and Muthén, B. O. (2012). Mplus User’s Guide. Los Angeles, CA:
Muthen & Muthen.
Nikolaou, I., and Georgiou, K. (2018). Fairness reactions to the employment
interview. J. Work Organ. Psychol. 34, 103–111. doi: 10.5093/jwop2018a13
Nørskov, S., and Ulhøi, J. P. (2020). “The use of robots in job interviews,” in
Encyclopedia of Electronic HRM, eds S. Fisher, and T. Bondarouk (Berlin: De
Gruyter), 208–213. doi: 10.1515/9783110633702-032
Papadopoulos, F., Dautenhahn, K., and Ho, W. C. (2012). Exploring the use
of robots as social mediators in a remote human-human collaborative
communication experiment. Paladyn 3, 1–10. doi: 10.2478/s13230-012-
0018-z
Paulhus, D. L., and Morgan, K. L. (1997). Perceptions of intelligence in leaderless
groups: the dynamic effects of shyness and acquaintance. J. Pers. Soc. Psychol.
72, 581–591. doi: 10.1037/0022-3514.72.3.581
Paulhus, D. L., Westlake, B. G., Calvez, S. S., and Harms, P. D. (2013). Self-
presentation style in job interviews: the role of personality and culture. J. Appl.
Soc. Psychol. 43, 2042–2059. doi: 10.1111/jasp.12157
Frontiers in Robotics and AI | www.frontiersin.org 17 November 2020 | Volume 7 | Article 586263
Nørskov et al. Robot-Mediated Interview and Fairness
Ployhart, R. E., and Oswald, F. L. (2004). Applications of mean and covariance
structure analysis: integrating correlational and experimental approaches.
Organ. Res. Methods 7, 27–65. doi: 10.1177/1094428103259554
Proost, K., Schreurs, B., De Witte, K., and Derous, E. (2010). Ingratiation and
self-promotion in the selection interview: the effects of using single tactics or
a combination of tactics on interviewer judgments. J. Appl. Soc. Psychol. 40,
2155–2169. doi: 10.1111/j.1559-1816.2010.00654.x
Purkiss, S. L. S., Perrewe, P. L., Gillespie, T. L., Mayes, B. T., and
Ferris, G. R. (2006). Implicit sources of bias in employment interview
judgments and decisions. Organ. Behav. Hum. Decis. Process 101, 152–167.
doi: 10.1016/j.obhdp.2006.06.005
Rivera, L. A. (2012). Hiring as cultural matching: the case of elite professional
service firms. Am. Sociol. Rev. 77, 999–1022. doi: 10.1177/0003122412463213
Rivera, L. A. (2015). Go with your gut: emotion and evaluation in job interviews.
Am. J. Sociol. 120, 1339–1389. doi: 10.1086/681214
Ruffle, B. J., and Shtudiner, Z. (2015). Are good-looking people more employable?
Manage. Sci. 61, 1760–1776. doi: 10.1287/mnsc.2014.1927
Ryan, A. M., and Huth, M. (2008). Not much more than platitudes? A critical look
at the utility of applicant reactions research. Hum. Resour. Manage. Rev. 18,
119–132. doi: 10.1016/j.hrmr.2008.07.004
Ryan, A. M., and Ployhart, R. E. (2000). Applicants’ perception of selection
procedures and decisions: a critical review and agenda for the future. J. Manag.
26, 565–606. doi: 10.1177/014920630002600308
Rynes, S. L. (1993). “Who’s selecting whom? Effects of selection practices on
applicant attitudes and behavior,” in Personnel Selection in Organizations, eds
N. Schmitt, and W. C. Borman (San Francisco, CA: Jossey-Bass), 240–274.
Satorra, A., and Bentler, P. M. (2001). A scaled difference chi-square
test statistic for moment structure analysis. Psychometrika 66, 507–514.
doi: 10.1007/BF02296192
Schreurs, B., Hamstra, M., Segers, M., and Schmitte, K. (2018). Where to
seat the applicant? How spatial distance influences the effect of self-
promotion on interviewer evaluations. J. Appl. Soc. Psychol. 48, 448–456.
doi: 10.1111/jasp.12524
Schuler, H. (1993). “Social validity of selection situations: a concept and some
empirical results,” in Personnel Selection and Assessment: Individual and
Organizational Perspectives, eds J. Schuler, J. L. Farr, and M. Smith (Hillsdale,
NJ: Erlbaum), 41–55.
Sears, G., Zhang, H., Wiesner, H. W., Hackett, R. D., and Yuan, Y. (2013).
A comparative assessment of videoconference and face-to-face employment
interviews. Manage. Decis. 51, 1733–1752. doi: 10.1108/MD-09-2012-0642
Seibt, J., and Vestergaard, C. (2018). Fair proxy communication: using social robots
to modify the mechanisms of implicit social cognition. Res. Ideas Outcomes
4:e31827. doi: 10.3897/rio.4.e31827
Seo, S. H., Geiskkovitch, D., Nakane, M., King, C., and Young, J. E. (2015). “Poor
thing! Would you feel sorry for a simulated robot? A comparison of empathy
toward a physical and a simulated robot,” in Proceedings of the Tenth Annual
ACM/IEEE International Conference on Human-Robot Interaction (Portland,
OR). doi: 10.1145/2696454.2696471
Silvester, J., and Anderson, N. (2003). Technology and discourse: a comparison
of face-to-face and telephone employment interviews. Int. J. Sel. Assess. 11,
206–214. doi: 10.1111/1468-2389.00244
Smedegaard, C. V. (2019). “Reframing the role of novelty within social HRI: from
noise to information,” in 14th ACM/IEEE International Conference on Human-
Robot Interaction (HRI) (Daegu), 411–420. doi: 10.1109/HRI.2019.8673219
Smith, A., and Rogers, V. (2000). Ethics-related responses to specific situation
vignettes: evidence of gender-based differences and occupational socialization.
J. Bus. Ethics 28, 73–86. doi: 10.1023/A:1006388923834
Steiger, J. H. (2016). Notes on the Steiger–Lind (1980) Handout. Struct. Eq. Model.
Multidiscipl. J. 23, 777–781. doi: 10.1080/10705511.2016.1217487
Steiger, J. H., and Lind J. M. (1980). “Statistically based tests for the number of
common factors,” in Paper presented at the Meeting of the Psychometric Society
(Iowa City, IA).
Stritzke, W. G., Nguyen, A., and Durkin, K. (2004). Shyness and computer-
mediated communication: a self-presentational theory perspective. Media
Psychol. 6, 1–22. doi: 10.1207/s1532785xmep0601_1
Suen, H. Y., Chen, M. Y. C., and Lu, S. H. (2019). Does the use of synchrony
and artificial intelligence in video interviews affect interview ratings and
applicant attitudes? Comput. Hum. Behav. 98, 93–101. doi: 10.1016/j.chb.2019.
04.012
Swann, W. B., and Rentfrow, P. J. (2001). Blirtatiousness: cognitive, behavioral,
and physiological consequences of rapid responding. J. Pers. Soc. Psychol. 81,
1160–1175. doi: 10.1037/0022-3514.81.6.1160
Sylva, H., and Mol, S. T. (2009). E-Recruitment: a study into applicant
perceptions of an online application system. Int. J. Sel. Assess. 17, 311–323.
doi: 10.1111/j.1468-2389.2009.00473.x
Tanaka, K., Nakanishi, H., and Ishiguro, H. (2014). “Comparing video, avatar,
and robot mediated communication: pros and cons of embodiment,”
in Collaboration Technologies and Social Computing. CollabTech 2014.
Communications in Computer and Information Science, Vol 460, eds T.
Yuizono, G. Zurita, N. Baloian, T. Inoue, and H. Ogata (Berlin, Heidelberg:
Springer), 96–110. doi: 10.1007/978-3-662-44651-5_9
Thibaut, J., and Walker, L. (1975). Procedural Justice: A Psychological Analysis.
Hillsdale, NJ: Erlbaum.
Tidwell, L. C., and Walther, J. B. (2002). Computer-mediated communication
effects on disclosure, impressions, and interpersonal evaluations: getting to
know one another a bit at a time. Hum. Commun. Res. 28, 317–348.
doi: 10.1111/j.1468-2958.2002.tb00811.x
Trope, Y., and Liberman, N. (2010). Construal-level theory of psychological
distance. Psychol. Rev. 117, 440–463. doi: 10.1037/a0018963
Tucker, L. R., and Lewis, C. (1973). A reliability coefficient for maximum likelihood
factor analysis. Psychometrika 38, 1–10. doi: 10.1007/BF02291170
Turban, D. B., and Dougherty, T. W. (1992). Influences of campus recruiting on
applicant attraction to firms. Acad. Manage. J. 35, 739–765. doi: 10.2307/256314
Turkle, S. (2011). Alone Together: Why We Expect More From Technology and Less
From Each Other. New York, NY: Basic Books.
Vallor, S. (2015). Moral deskilling and upskilling in a new machine age:
reflections on the ambiguous future of character. Philos. Technol. 28, 107–124.
doi: 10.1007/s13347-014-0156-9
Van Wynsberghe, A. (2013). Designing robots for care: care centered value-
sensitive design. Sci. Eng. Ethics 19, 407–433. doi: 10.1007/s11948-011-9343-6
Walther, J. B. (1996). Computer-mediated communication: impersonal,
interpersonal, and hyperpersonal interaction. Commun. Res. 23, 3–43.
doi: 10.1177/009365096023001001
Wang, B., and Rau, P.-L. P. (2019). Influence of embodiment and substrate of social
robots on users’ decision-making and attitude. Int. J. Soc. Rob. 11, 411–421.
doi: 10.1007/s12369-018-0510-7
Wang, S.-P., Lai, C.-T., Huang, A.-J., and Wang, H.-C. (2014). “KinChat: veiling
your face without suppressing facial expression in text communication,” in
Paper presented at the CHI’14 Extended Abstracts on Human Factors in
Computing Systems (Toronto, ON). doi: 10.1145/2559206.2581160
Wason, K. D., Polonsky, M. J., and Hyman, M. R. (2002). Designing
vignette studies in marketing. Australas. Market. J. 10, 41–58.
doi: 10.1016/S1441-3582(02)70157-2
Woods, S. A., Ahmed, S., Nikolaou, I., Costa, A. C., and Anderson, N. R. (2020).
Personnel selection in the digital age: a review of validity and applicant
reactions, and future research challenges. Eur. J. Work Organ. Psychol. 29,
64–77. doi: 10.1080/1359432X.2019.1681401
Xu, K. (2019). First encounter with robot alpha: how individual differences interact
with vocal and kinetic cues in users’ social responses. New Media Soc. 21,
2522–2547. doi: 10.1177/1461444819851479
Zajonc, R. B. (1980). Feeling and thinking: preferences need no inferences. Am.
Psychol. 35, 151–175. doi: 10.1037/0003-066X.35.2.151
Conflict of Interest: The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be construed as a
potential conflict of interest.
Copyright © 2020 Nørskov, Damholdt, Ulhøi, Jensen, Ess and Seibt. This is an open-
access article distributed under the terms of the Creative Commons Attribution
License (CC BY). The use, distribution or reproduction in other forums is permitted,
provided the original author(s) and the copyright owner(s) are credited and that the
original publication in this journal is cited, in accordance with accepted academic
practice. No use, distribution or reproduction is permitted which does not comply
with these terms.
Frontiers in Robotics and AI | www.frontiersin.org 18 November 2020 | Volume 7 | Article 586263
Content uploaded by Sladjana Nørskov
Author content
All content in this area was uploaded by Sladjana Nørskov on Nov 11, 2020
Content may be subject to copyright.
