Index
Franziska Leutner
(Goldsmiths, University of London, UK)
Reece Akhtar
(Deeper Signals, USA)
Tomas Chamorro-Premuzic
(University College London, UK)
ISBN: 978-1-83867-562-2, eISBN: 978-1-83867-559-2
Publication date: 11 March 2022
This content is currently only available as a PDF
Citation
Leutner, F., Akhtar, R. and Chamorro-Premuzic, T. (2022), "Index", The Future of Recruitment (The Future of Work), Emerald Publishing Limited, Leeds, pp. 197-207. https://doi.org/10.1108/978-1-83867-559-220221009
Publisher
:Emerald Publishing Limited
Copyright © 2022 by Emerald Publishing Limited
INDEX
Ability
, 24
Academic achievement
, 127
Accuracy
face recognition
, 74–75
of video interview algorithms
, 73–75
video interviews
, 66–75
voice recognition
, 74–75
Adverse impact
, 37–38
extensive adverse impact testing
, 68
of video interview algorithms
, 73–75
Agreeableness
, 28–29
Algorithmic analysis of video interview assessments
, 69–70
Algorithmic fairness
, 57–58
Algorithmic HR
, 38–41
Algorithmic responsibility
, 40–41
Algorithms
, 35–36, 51–52
Amazon
, 162–163
Ambition
, 24, 159–160
Anonymity
, 153–154
Anthropogenic disasters
, 148
Artificial intelligence (AI)
, 17, 90, 92, 183–184, 188
AI-powered talent algorithms
, 30
algorithms
, 98
to assessment and recruitment
, 161–162
ethics framework
, 155–156
in future of recruitment
, 32
GPS navigator
, 161–162
models
, 74–75
in recruitment
, 147
talent assessment in age of
, 30–35
Artificial neural network
, 127–128
Assessment algorithms
, 40
Automated assessment system
, 56
Automated coaching and development
, 103
Automated scoring algorithms
, 59–60
Automation technology
, 18–19, 188
Bag of words approaches
, 60–61
Balloon Analogue Risk Task
, 130–131
Behavioral observations
, 67–68
BetterUp company
, 103
Biases
, 26
of assessments
, 176–177
and discrimination
, 105
outcome
, 161–162
social desirability
, 128–129
Bidirectional Encoder Representations from Transformer (BERT)
, 62–63
Big data
, 33
Big Five personality traits
, 58–59, 94–95, 128, 131–132
Biodata
, 26–28
Biographical data
, 27
Black box algorithms
, 37–38
Black Mirror
, 11
C-Suite
, 22
Cambridge Analytica scandal
, 104–105
Candidate
evaluation of
, 54
experience
, 9–10, 51–52
game-based assessments
, 99–100
interviews and
, 66
rebalancing relationship between employer and
, 186–187
reliability
, 71–72
social media profiles
, 31
Careers
, 2
advice
, 180
benefits for
, 20–21
career-related self-awareness
, 12–13
choices
, 1–2
evolution in
, 3–4
fulfilling
, 179–180
ideally
, 2–3
success
, 173–174
Classic I-O tenet
, 23
Clinical impairment
, 127
Cognitive ability
, 176–177
game-based assessments for measuring
, 124–128
predicting real world outcomes using cognitive ability game-based assessments
, 127–128
tests
, 122–124, 176–177
Cognitive orientations
, 61
Communication
digital forms of
, 96–97
external
, 149–151
human
, 63–64
Competencies
, 58, 69, 74
Computational psychometrics
, 51–53
Computer scoring of video interviews
, 56
Confidentiality
, 153–154
Conscientiousness
, 24, 28–29
Content validity
, 68–69
Context in natural language processing
, 62–63
Convergence
, 6
Convergent validity
, 68, 70–71
Coring methodology
, 27
Correlations
, 70–71
Counterproductive work behaviors
, 19–20
Covid-19
, 21
Culture fit
, 4–5, 98–99
improving diversity and
, 101–102
CultureAmp
, 99, 102
CultureX
, 99
Cyborgs
, 148
Data protection
, 153–154
Data revolution
, 18, 22, 24, 31–34, 40–41
Data science revolution
, 8
Data-driven assessments
, 37–38
Decision making
AI in
, 34–35
hiring manager
, 53
human bias effect in
, 175–176
monitoring and adjusting
, 57–58
Deeper Signals company
, 103
Demographic signals
, 151–153
Digital footprint
, 93–94
Digital footprint-powered talent assessments
, 95
Digital interviews. See Video interviews
“Digital nomadism”
, 21
Digital panopticon
, 104–106
Digital platforms and devices
, 31
Digital records
, 101–102
Digital records of recruitment
, 32
Digital services, advancements in
, 90
Digital talent signal mining
advancements in technology and digital services
, 90
connected technologies
, 89
digital panopticon
, 104–106
ethical risks and limitations
, 104–106
mining online behavior
, 92
natural language processing and talent signals
, 96–100
relationship between online behavior and psychological variables
, 92–93
talent assessments
, 91–92
uses and applications
, 100–104
Digital technology
, 17
“Digital transformation”
, 21
Digitalization
, 183–184
Directly predicting job performance
, 52
Diversity
, 4–5, 174–179
improving
, 101–102
in workforce
, 173–174
DropBox
, 21
Effortless performance rule
, 23
Ekman’s theory
, 65
Email
, 96–97
Emotion(al)
framework
, 65
management
, 131–132
stability
, 28–29
Employee
digital footprints
, 33–34
engagement
, 98–99
Employer, rebalancing relationship between candidate and
, 186–187
Employment
and interviews
, 25–26
self-employment
, 20–21
traditional forms of
, 20–21
Enthusiasm
, 19
Ethics of future recruitment tools
, 10–11
Evidence-based selection practices
, 24–25
Explainability
, 155–156
Extensive adverse impact testing
, 68
Extroversion
, 28–29
Ezra company
, 103
Face analysis algorithms
, 74–75
Face recognition accuracy
, 74–75
Face-scanning technologies
, 151–153
Facebook
, 33, 92–93, 96–97, 162–163
“Facial action coding” system
, 65
Feature learning algorithms
, 65
Feedback
, 154–155
“Five Factor Model”
, 28–29
Fiverr platform
, 20–21, 103–104
Forced choice tests
, 129–130, 134–135
Freud’s psychoanalytic methodologies
, 96–97
Game features
, 122–123
Game technology
, 124
Game-based assessments
, 9–10, 119–120
on cognitive ability
, 120
improvement of traditional psychometric tests
, 124
for measuring cognitive ability
, 124–128
for personality
, 128–135
predicting real world outcomes using cognitive ability
, 127–128
for use in recruitment and selection
, 122–123
Game-based psychometric assessments
, 119–120
Gamification
, 9–10, 119–120
advantages
, 122–124
of personality
, 131–132
of psychometric tests
, 120–121
General Inquirer
, 61
Generalized artificial intelligence
, 34–35
Generation Z, employees from
, 173–174
Gig economy
, 20–21
Glassdoor
, 99
Glikon
, 102
Glint
, 102
Google
, 19
Great Brain Experiment
, 126–127
Harm reduction approach
, 37–38
Heuristics
, 26
HireVue
, 57–58
Hiring managers
, 34–35, 153–154, 183–184
decision making
, 53
resume screening
, 97–98
Human capital technologies
, 156–157
Human creativity
, 188
Human ratings prediction
, 70
Human resource (HR)
algorithmic responsibility
, 40–41
data literacy
, 39–40
departments
, 183–184
preparing for algorithmic
, 38–41
tech
, 156–157
tools
, 172
tracking performance and business impact
, 38–39
I-O psychologists
, 32
Images
, 132–134
Implicit heuristics
, 26
Inclusion
, 4–5
Individual differences research
, 52–53
Industrial-Organizational Psychology (I-O psychology)
, 8, 12
Informed consent
, 151–153
Intelligence
, 29
Intelligence tests
, 54–55
Interactive assessments
, 119
Internal recruitment
, 182–183
Internal talent assessment and analytics
, 102
International Personality Item Pool
, 128
Interviewers
, 175–176
Interviewing method
, 52–53
Interviews. See also Video interviews
, 25–26, 29
Ivy League qualifications
, 4
Job
benefits to job seeker
, 149–151
interviews
, 25, 183–184
search platforms
, 186–187
data
, 72–73
data prediction
, 70
Job performance
, 28–29, 52, 58, 74, 94–95, 123–124, 127, 176–177, 185
Knee-jerk reaction
, 34–35
Labels
, 61–62
Language
, 62–63, 96–97
agnostic nature of images
, 133–134
language-based assessment of personality
, 120
to quantifiable talent signals
, 97
use and content
, 61–62
Likert scale personality tests
, 128–129
Linguistic Inquiry and Word Count (LIWC)
, 62
LinkedIn
, 172, 186–187
Machine learning
, 51–52, 58–59, 74–75, 183–184, 188
algorithm
, 52–53, 132–133, 160–161
machine learning–based scoring algorithms
, 124
Man-made disasters
, 148
Matchmaking
, 185–186
Maximal performance rule
, 22–23
Meritocracy
, 174–179
Meta-analysis
, 27, 29, 55
Microsoft
, 19, 102
Modern game-based assessments
, 120–121
Motivation
, 52
Myers Briggs Type Indicator (MBTI)
, 157–158
Narcissistic applicants
, 175–176
Natural language processing
, 60–63
Bag of Words
, 60–61
context
, 62–63
language use and content
, 61–62
and talent signals
, 96–100
transcription
, 60
Natural Language Toolkit
, 61–62
Nepotism
, 3–4
Netflix
, 34–35
Newtons Playground (computer game)
, 130–131
Nonverbal behavior
, 58, 63, 66
facial action units
, 65–66
spectral audio characteristics
, 65
Nonverbal behavioral cues
, 52
“One-click” assessment
, 100–101
One-sided job application
, 187
Openness
, 28–29
Opportunity allocation
, 182–183
Organization-level engagement
, 20
Organizational psychologists
, 173
Outcome bias
, 161–162
Pareto’s principle
, 22
Patagonia
, 162–163
People Analytics
, 33–34
Performance management
, 184–185
Performance-based models
, 72–73
Personality
, 23, 52–55, 58–59
assessment
, 29
dark side personality traits
, 33
game-based assessments for
, 128–135
gamification of
, 131–132, 134
measuring
, 134–135
prediction of
, 95–96
taxonomies of
, 28–29
tests as games
, 130–135
in workplace
, 95–96
Predictive validity
, 29–30, 72–73
Predictors
, 27
Prehire assessments
, 100–101
Profiling psychometric traits
, 52
Psychological capital
, 4
Psychological research
, 92–93
Psychological trait
, 74
Psychology
, 2
Psychometric assessment
, 28–30, 54–56, 67–68, 171–172, 176–177, 183–184
video analytics replicates
, 54–55
Psychometric inventories
, 31–32
Psychometric standards
, 69, 73, 120–121
Psychometric surveys
, 91–92
Psychometric test
, 69
challenge for
, 121–122
gamification of
, 120–121
Psychometrics
, 28
Psychopathic applicants
, 175–176
Questionnaires
fatigue
, 123–124
forced choice
, 128–129
Recruiters
, 153–154
Recruitment
, 1–3, 147
adverse impact
, 160–161
AI in
, 147
“ambition”
, 159–160
anonymity
, 153–154
apocalyptic fears of cyborgs
, 148
benefits to job seeker
, 149–151
components of recruitment tools
, 156–157
confidentiality
, 153–154
data protection
, 153–154
digital records and AI in future of
, 32
dreams and possibilities for
, 173–183
evolution in careers
, 3–4
explainability
, 155–156
feedback and self-awareness
, 154–155
fulfilling career
, 179–180
future recruitment tools
, 148–156
gone
, 4–5
informed consent
, 151–153
innovation
, 7–11
matchmaking
, 185–186
meritocracy and diversity
, 174–179
outcome bias
, 161–162
performance management
, 184–185
performance measures
, 162–163
psychological research
, 2–3
psychologically safe and productive workplace
, 180–183
psychometric assessments
, 67–68
rebalancing relationship between employer and candidate
, 186–187
science-practice gap
, 157–158
task of
, 171–172
technologies of future
, 183–189
time
, 6
universal overarching principle parameters
, 158–159
virtue signaling
, 163–164
Red Bull Wingfinder assessment
, 132–133
Reliability
, 36–37, 71–72
Resume screening
, 97–98
Robust hiring practices
, 101–102
Rule of vital few
, 22
Scenario-based game assessment
, 131–132
Selection method
, 176–177
dreams and possibilities for
, 173–183
fulfilling career
, 179–180
matchmaking
, 185–186
meritocracy and diversity
, 174–179
performance management
, 184–185
psychologically safe and productive workplace
, 180–183
rebalancing relationship between employer and candidate
, 186–187
task of
, 171–172
technologies of future
, 183–189
Self report questionnaires
, 58–59
Self-awareness
, 154–155
Self-employment
, 20–21
Self-report
, 128–129
questionnaires
, 52–53
self-reported job performance
, 56–57
self-reported psychological traits prediction
, 70
Seminal meta-analysis
, 25–26
Sensor-packed devices
, 30
Serious games
, 119
Serious games
, 124–125
Sine-qua-non for ethical recruitment
, 157–158
Slack
, 33–34, 96–97
SMS
, 96–97
Social capital
, 3–4
Social media platforms
, 92–93, 96–97, 100–101, 186–187
Social skills
, 24
Spectral audio characteristics
, 65
Spotify
, 34–35
Standardized interviews
, 54–55
Standardized psychometric assessments
, 54–55
Storytelling
, 131–132
Structured interviews
, 52–53, 69, 171–172, 176–177
and standardized analysis at scale
, 55–56
Structured recruitment processes
, 54
Talent. See also Recruitment
, 23
advantages for
, 53–58
assessments
, 91–92, 99–100
better data
, 56–57
culture fit and employee engagement
, 98–99
current practices
, 24–30
identification
, 18
internal talent assessment and analytics
, 102
interviews
, 25–26
management interventions
, 23
monitoring and adjusting decision making
, 57–58
natural language processing and
, 96–100
passports
, 103–104
resume screening
, 97–98
signals
, 18
signals
, 151–153
structured interviews and standardized analysis at scale
, 55–56
talent assessment in age of AI
, 30–35
theory of talent signals
, 32, 90
understanding effectiveness of new tools
, 35–38
war for or on talent
, 18–24
Taxonomies of personality
, 28–29
Team-level engagement
, 20
Technological dystopias
, 11
Technological innovation
, 52–53
Technology
, 148, 156, 172–173
advancements in
, 90
to implement structured interviewing
, 56
Textio tool
, 98
Textkernel tool
, 98
Theory of talent signals
, 32, 90
Tracking performance
, 38–39
Traditional psychometric assessments
, 123–124
Traditional psychometric tests
, 120–121
Traditionally measure psychometric traits
, 52
Transcription algorithms
, 74–75
Transcription algorithms
, 60
Twitter
, 94–95, 172, 186
Uber
, 20–21, 162–163
Unemployment. See also Employment
, 1–2
Upwork platform
, 20–21, 103–104
Validity
, 36–37
coefficient for interviews
, 25–26
Verbal behavior
, 58
Video analytics
, 51, 53–54
Video interviews
, 54, 120, 151, 153, 171–172
advantages for talent identification
, 53–58
adverse impact and accuracy of video interview algorithms
, 73–75
algorithm
, 159
analytics
, 58, 66, 70
computer scoring of
, 56
fairness and accuracy across groups
, 66–75
natural language processing
, 60–63
nonverbal behavior
, 63–66
practical implications
, 75–76
psychometric standards
, 69–73
and video analytics
, 51–53
words
, 58–60
Virtue signaling
, 163–164
Voice recognition accuracy
, 74–75
War for talent
, 18–24
War on talent
, 18–24
Washington Post, The
, 151–153
Weapons of math destruction (WMDs)
, 40
“Web 2. 0”
, 92–93
Webcams
, 51–52
Wechsler Adult Intelligence scales
, 126–127
WhatsApp
, 96–97
Wikipedia
, 89
Word Error Rate
, 60
Words
, 58–60
Work
automation
, 188–189
performance prediction
, 27
work-life balance
, 21–22
work-related data
, 187
Workforce, diversity in
, 173–174
Workplace
personality in
, 95–96
psychologically safe and productive
, 180–183
YouTube
, 89, 156–157
Zoom app
, 21, 33–34, 151, 153