Index
The Adoption and Effect of Artificial Intelligence on Human Resources Management, Part B
ISBN: 978-1-80455-663-4, eISBN: 978-1-80455-662-7
Publication date: 10 February 2023
This content is currently only available as a PDF
Citation
(2023), "Index", Tyagi, P., Chilamkurti, N., Grima, S., Sood, K. and Balusamy, B. (Ed.) The Adoption and Effect of Artificial Intelligence on Human Resources Management, Part B (Emerald Studies in Finance, Insurance, and Risk Management), Emerald Publishing Limited, Leeds, pp. 251-259. https://doi.org/10.1108/978-1-80455-662-720230015
Publisher
:Emerald Publishing Limited
Copyright © 2023 Pallavi Tyagi, Naveen Chilamkurti, Simon Grima, Kiran Sood and Balamurugan Balusamy
INDEX
Acceptability of employees
, 56–57
Accountability
, 46
of employees
, 56–57
system
, 60
Adaptive network-based fuzzy Interface systems (ANTIS)
, 131
Adobe Company
, 201
Adoption of Artificial intelligence in HR practices
administration of HR
, 70
AI
, 68
AI and HRM
, 68–69
communalities
, 75
data analysis
, 72
descriptive statistics, reliability, and correlations
, 72–75
development and learning
, 70–71
factor loading under Varimax rotation
, 76
importance of research
, 66–67
KMO and Bartlett’s Test
, 74
literature and hypothesis development
, 68
management of talent
, 71
measures
, 72
need for AI in HRM
, 67
onboarding
, 70
purpose of study
, 68
research methodology
, 71–72
result
, 72
sampling and procedure
, 71–72
talent acquisition
, 69–70
Agnostic Chabot technology
, 66
Agriculture
, 188
Airbnb (Company)
, 222
Ajzen and Fishbein’s Theory of Reasoned Action (Ajzen and Fishbein’s TRA)
, 47
Alta program
, 227
Amazon (Company)
, 222
Amazon’s AI recruiting tool, failure of
, 95
American AI Initiative
, 187
Analytical software
, 159
Analytics
, 240
Ancillary benefits
, 2
Application Tracking System (ATS)
, 6
Artificial intelligence (AI)
, 2, 14, 33, 35–36, 68, 86, 88, 112, 140, 128, 182–184, 201, 222–224
accelerate competitive advantages
, 116
adoption of different AI-empowered tools in talent acquisition by select companies
, 226–227
advantages of
, 226
advantages over current modalities in education sector
, 165–166
AI market size/revenue comparisons 2018–2027
, 137
AI Market Spending Worldwide 2020
, 138
AI Timeline
, 141
AI-assisted IT solutions
, 120
AI-based algorithms
, 83, 93, 159
AI-based chatbots
, 85
AI-based classrooms
, 167
AI-based HRM technologies
, 225
AI-based programs
, 154
AI-based teaching system
, 167
AI-based technologies
, 151
AI-based tools
, 165
AI-derived teaching
, 167
AI-enabled chatbots
, 4, 132
AI/ML Project
, 187
AI/RPA adoption
, 137
algorithms
, 163, 165
better employee experience
, 117
better employee relationship management
, 116
better workplace learning
, 119
challenges
, 47–53, 61
challenges encountered by HRM in adopting
, 120–122
change in AI investment in global companies
, 139
chatbot in HR
, 116
companies using AI in training and development
, 227–229
current limitations of AI in learning and development
, 166–168
definition of AI
, 183–185
effective learning and development
, 117
effective utilisation of HR budgeting
, 117
and employee engagement
, 230–232
employees’ acceptance of AI practices in performance appraisal
, 55–57
employers’ accountability and performance appraisal using
, 57–60
enhance efficiency
, 119
global business and HR Leaders on AI impact to job numbers
, 140
in healthcare learning, development, and delivery
, 164–165
in HR functions
, 47–48
in HRM
, 3, 68–69
in HRM practices, benefits of
, 115
in HRM Practices, role of
, 117–119
in human resources and interview preparation
, 158–160
hypothesis
, 135
interview process
, 119
investment
, 140
and ITS utility
, 152–153
job replacement theory
, 187
in learning and development
, 50
less administrative burden
, 119
less human bias
, 116
Loreal Case
, 7–10
in medical education and training
, 160–162
methodology
, 134–142, 151–152, 223
need for AI in HRM
, 67
objectives
, 134
onboarding
, 119
organisational structure
, 50
in performance appraisal
, 50
and performance management
, 229–230
promotion of inclusion and equality
, 118
recruitment
, 118
for recruitment and selection
in recruitment and selection process
, 49
reduce discrimination
, 119
reduction in employee turnover
, 118
requirement of AI in organisation
, 185–186
in research and development
, 163–164
results and analysis
, 135
review of literature
, 132
scanning resumes
, 118
in school, college and technical education
, 156–157
selection
, 119
significance of
, 224–225
streamlined process
, 115
and talent acquisition
, 225–227
techniques
, 116
technology
, 2, 66
tools in re-inventing HRM
, 225
and training and development
, 227
understanding employee referrals
, 117
utility of AI in learning and development
, 153–165
Artificial neural networks
, 151
Augmented reality (AR)
, 162
Automated responding machine (see Chatbox system)
Automated tools
, 36
Automation
, 53
technology
, 188
Automotive industry
, 153
Barlett’s test of sphericity
, 74
Base theory
, 47
Behaviour mapping
, 231
Benefits of using AI technology tools
, 8
Big data
, 35, 152
analytics
, 112
Biometrics
, 3
Blockchain
, 112
Book bots
, 35
Boundaryless career
, 34n3, 37–38
Brazen (recruiting chatbot)
, 5–6
British NHS
, 116
Business intelligence (BI)
, 184, 195
Business leaders
, 182
Businesses
, 182
Centre for Monitoring Indian Economy (CMIE)
, 135
Challenges
of AI
, 82
of using AI technology tools
, 9
Change nature of work
, 35–36
Chatbots
, 3, 8, 231
in HR
, 116
system
, 118
Cisco
, 226
Cloud technique
, 244
Cognitive skills essential for upskilling and reskilling
, 194–195
Collaboration tools
, 231
College, AI in
, 156–157
Complex Cognitive Skills
, 194
Complex system
, 34
Complexity
, 34
Computers
, 119
vision
, 152–153
Continuous learning skills
, 195
Contradictions
, 47
Conventional methods
, 229
COVID-19
, 32, 163, 190
induced uncertainty
, 128
pandemic
, 142, 222
Creativity (CR)
, 18
Customer-focused process
, 214
Cutting-edge technology
, 112
Data analysis
, 20–22, 72, 203
ANOVA for change acceptance factors
, 24
demographic study
, 22–24
KMO and Bartlett’s Test
, 21
rotated component matrix
, 23
skills
, 194
Data analytics
, 152, 159
Data collection
, 51, 238
Data identification
, 238
Data inclusion
, 240
De-contextualisation
, 56
Decision-making process
, 66, 83
skills
, 195
Deep learning
, 85, 88, 151, 152, 163, 165
Deloitte (companies)
, 135, 232
Deloitte’s 2019 Global Human Capital Trends report
, 15
Digital employees’
, 201
Digital Era’
, 200
Digital expertise
, 53
Digital services
, 201
Digital skills
, 194
Digital technologies
, 118–119, 202
Digital tools
, 209
Digital transformation’
, 200
in HRM’
, 202
Digitalisation process
, 200, 202, 208–212
advantages and disadvantages
, 212–213
in HRM’
, 202
pre-requisites for implementing digitalisation in HRM
, 213
significances of HRM digitalisation
, 212
Diversity
, 236
Dynamic skill theory
, 186
E-HRM
, 203
E-learning tools
, 159
E-recruitment
, 204, 206
Economist Robert Gordon
, 182
Education
, 148, 157, 189
advantages of AI over current modalities in
, 165–166
sector
, 148
system
, 153
Educational data mining (EDM)
, 166
Educational institutions
, 153
Educational systems
, 156
Educators
, 157
Electronic HR systems (e-HR systems)
, 66
Emotional intelligence
, 69
Empirical analysis
, 74
Employees
, 159, 191, 201
acceptability and accountability of employees
, 56–57
AI and Employee engagement
, 230
base theory and contradictions
, 47
challenges of AI
, 47–53
companies
, 231–232
data review
, 58–59
employees’ acceptance of AI practices in performance appraisal
, 55
employers’ accountability and performance appraisal using AI
, 57–61
existing models on change readiness of
, 19–20
getting personal with technology
, 53–55
HR practices and Employees’ acceptability and accountability
referrals
, 117
relationship management
, 116
responsibility
, 57
Employers’ accountability using AI
, 57–61
Employers’ performance appraisal using AI
, 57–61
Energy
, 189
Equality, promotion of
, 118
Evidence-based approach’
, 246
Explainability
, 55
Explicit instruction (EI)
, 165
Facebook
, 226
messengers
, 132
Finance
, 115
Fuzzy artificial neural networks (FANN)
, 131
Fuzzy transaction data-mining algorithm (MFTDA)
, 131
Game theory
, 3
Generation gap (GG)
, 39
Genetic algorithms
, 85, 151
Global Human Capital Trends
, 131
Globalisation
, 82, 236
Google
, 222
Harvard Business Review (HBS)
, 54, 141
Health care
, 188
AI in healthcare learning, development, and delivery
, 164–165
industry
, 160, 165
Hub-and-spoke’ strategy
, 123
Human cognitive process 114
Human intellectual processes
, 184
Human intelligence
, 2
Human resource drivers (HRD)
, 71
Human Resource Information Systems (HRIS)
, 17, 66
Human resource management (HRM)
, 2, 33, 68–69, 88, 133, 158, 238, 240
adopting artificial technology in Human resource management practices
analytics
, 245
articles selected for review
, 203–207
benefits of AI in
, 115–117
challenges encountered by HRM in adopting AI
, 120–122
change mindset
, 122
department
, 40
digitalisation
, 202, 208–216
employee’s fear
, 122
ethical and work culture decisions
, 120–121
financial constrain
, 122
implement ‘hub-and-spoke’ strategy
, 123
implications
, 216
improve data quality and quantity
, 123
inadequate-proven applications
, 121
integrate AI With Cloud
, 123
integration capabilities
, 121
lack of appropriate data
, 121
lack of understanding
, 120
limitation of study
, 123–124
literature review
, 114–115, 202
management is apprehensive about having to replace old systems
, 121
need for AI in
, 67
ongoing management
, 121
practical implication
, 216–217
pre-requisites for implementing digitalisation in
, 213
privacy concerns
, 121
proper alignment
, 122
reduce biasness
, 122
research implications
, 216
results
, 207–208
robust IT system
, 120
role of AI in
, 117–119
search for AI experts
, 120
significance of study
, 115
significances of HRM digitalisation
, 212
strategies to overcome challenges
, 122
struggles to find effective vendor
, 121
traits of
, 112–114
transparency strategy
, 123
understand ethical issues
, 122
unstructured data
, 120
Human resources (HR)
, 115, 158, 225, 236, 238, 240
administration of
, 70
AI in Human resources and interview preparation
, 158–160
analysis based on metadata
, 241–246
analytics
, 236, 238, 240
application of AI in
, 17–18
assessing relevant literatures
, 88
challenges of AI implementation in HRM
, 90–92
chatbot in
, 116
data analysis
, 20–22
department global companies
, 57
descriptive analysis
, 89
developing search criteria
, 88
development of HR analytics research
, 242–243
effective utilisation of HR budgeting
, 117
existing models on change readiness of employees
, 19–20
extracting main content
, 88
factors affecting acceptance of AI
, 18
functions
, 134, 223
functions
, 47–48, 201
high investment
, 93
in implementing Artificial Intelligence
intelligent assistants
, 83
lack of digital infrastructure
, 93
lack of government regulations
, 94
lack of talents with appropriate skills
, 95
lack of top management support
, 93–94
lack of trust
, 90–93
less relevance of insights
, 94
managers
, 216–217
observations + recommendations
, 241
practices
, 36–37
practices by employees
professionals
, 52
research design
, 20
research methodology
, 86–88, 238–240
research questions
, 238
resistance to change
, 93
review of literature
, 16
search databases and keywords
, 88
secondary case study
, 95–96
selecting study dimensions
, 88
system
, 211
trends
, 246–248
Human-like traits
, 14
Human–computer interaction (HCI)
, 67
Human–machine collaborations
, 141
Humanoid machine
, 14
IBM (company)
, 113
IDC
, 135
Inclusion
, 236
promotion of
, 118
Information and communication technology (ICT)
, 33
Information technology (IT)
, 115, 201, 236
in HRM theory
, 77
Initial screening
, 240
Intelligent algorithms
, 159
Intelligent automation (IA)
, 131
Intelligent search
, 3
algorithms
, 3
Intelligent teaching tools
, 167
Intelligent technologies in HRM
, 160
Intelligent tutoring systems
, 162, 166
International Federation of Robotics
, 137
International Telecommunication Union
, 33
Internet of things
, 112
Interview process
, 119
ITS utility, AI and
, 152–153
Kaiser–Meyer–Oklin (KMO)
, 74
Labour force–displacement
, 187
Learners
, 157
Learning
, 148
development and
, 70–71
tools
, 167
Learning and development (LD)
, 50, 70, 117, 163
current limitations of AI in
, 166–168
utility of AI in
, 153–156
Lineage development
, 151
Lloyds Banking Group (LBG)
, 192
Logic Theorist
, 152
Logistics
, 189
Loreal case
, 7–10
Low-interaction employment
, 182
Low-skilled employment
, 182
Machine learning (ML)
, 14, 49, 151–152, 159, 165, 167, 167, 183, 224
and AI
, 161
Machine-based recruitment
, 49
Marketing
, 115
Massive open online courses (MOOC)
, 166
Mc Kinsey
, 135
Medical education
, 160
AI in Medical education and training
, 160–162
Metadata, analysis based on
, 241
development of HR analytics research
, 242–243
journals-wise publications
, 243–244
keywords used in title
, 244–246
publications based on ‘year’
, 241–242
Microsoft’s ‘MyAnalytics’ (personalized dashboard)
, 83
Mobile apps
, 112
Mobile sensors
, 36
Model’s efficiency
, 215
Monster Salary Index
, 54
Mya (recruiting chatbot)
, 6
Nanotechnologies
, 35
Natural language processing (NLP)
, 14, 151–152, 164
natural language processing-based technology
, 118
Netflix
, 153, 222, 226
Neural network
, 85, 88, 152, 153, 162, 183
North Carolina (NC)
, 35
Olivia (product of Paradox)
, 4–5
Onboarding process (OB process)
, 70, 119
Open Learning Initiative (OLI)
, 151
Optical character reader
, 3
Organisation
, 90, 202
system
, 51
Organisational readiness for AI
, 18
Pattern recognition
, 85
Paul Formosa’s research study
, 37
PD@GE tool
, 230
People analytic
, 236
Perceived ease of use (PEOU)
, 209
Performance appraisal
, 50–51
employees’ acceptance of AI practices in
, 55–57
Performance management, AI and
, 229
companies
, 230
Performance Management System (PMS)
, 229
Pre-requisites for implementing digitalisation in HRM
, 213
Preactor’ (software)
, 94
Predictive analytics
, 231
Problem-solving
, 14
Production
, 115
Protean career
, 34n2, 37–38
Psychological contract
, 37–38
Pymetrics
, 7
Re-inventing HRM, AI tools in
, 225
Real-time feedback tools
, 231
Recruitment
, 47, 118
process
, 2, 9, 49, 92, 129
Recruitment chatbots
, 3
Relational Database Management System (RDBMS)
, 120
Research design
, 20
Research papers considered for conducting systematic literature review
, 105–109
Reskilling
cognitive skills essential for upskilling and reskilling
, 194–195
definition of AI
, 183–186
evergreen skills for upskilling and reskilling
, 195
and ML
, 193
problem statement
, 186–187
skills necessary for upskilling and reskilling workforce
, 194
technological skills vital for upskilling and reskilling
, 194
upskilling and
, 187–193
Responsive feedback tools
, 231
Retail
, 189
Returns on investment (ROI)
, 237
Robotic automation
hypothesis
, 135
methodology
, 134–142
objectives
, 134
results and analysis
, 135
review of literature
, 132
Robotics
, 35, 140, 152
Robots
, 36, 152
Sarah Banking
, 37
Scanning resumes
, 118
School, AI in
, 156–157
Screening initial data
, 240
Secondary material sources
, 114
Selection
, 47
process
, 49
Self-efficacy (SE)
, 18
Self-learning
, 14
Sentiment analysis
, 93
Skilling
, 189
Skills necessary for upskilling and reskilling workforce
, 194
Small data
, 54
Smart cities
, 189
Social data analytics
, 35
Social media scrapping tool
, 6
Social robotics
, 33
Software
, 159
Speech recognition
, 85
Start-ups witnessed growth
, 201
Statista
, 135
Strata
, 135
Strategic Human Resource Management (SHRM)
, 51
Streamlined process
, 115
Students
, 157
Systematic literature review
, 82
Systematic survey approach
, 20
Talent acquisition (TA)
, 67, 69–70
adoption of different AI-empowered tools in talent acquisition by select companies
, 226–227
advantages of
, 226
AI and
, 225
Talent gap
, 204
Talent management (TM)
, 71
Teachers
, 154, 157
Technical education
, 166
AI in
, 156–157
Technological bias
, 55
Technological skills vital for upskilling and reskilling
, 194
Technology
, 39, 82, 165–166, 200
additional skills to impart
, 53–55
and change nature of work
, 35–36
getting personal with
, 53
HR Practices
, 36–37
implications
, 40
literature review
, 35
psychological contract, protean career, and boundaryless career
, 37–38
Technology Adoption Model (TAM)
, 19, 47
Thinking machines
, 182
3D printing
, 36
TOE–TAM integration
, 19
TOP model
, 203, 210
Tractica
, 135
Training
, 148
AI and
, 227
and development
, 61
Transition management theory
, 34
Transparency
, 55
Transportation
, 189
Trust
, 55
Uber
, 37
Uncertainty (UC)
, 17
Unilever (companies)
, 7
Upskilling and reskilling
, 187
cognitive skills essential for
, 194–195
evergreen skills for
, 195
findings
, 188
objectives
, 188
research methodology
, 188
skills necessary for upskilling workforce
, 194
technological skills vital for
, 192
UTAUT (technology models)
, 25
Varimax rotation
, 74
Virtual learning
, 154
Virtual reality (VR)
, 154, 162
Workforce analysis
, 236
Working from home (WFH)
, 53
World Bank
, 35
report
, 34
World Economic Forum (WEF)
, 187
World Health Organisation
, 32, 222
Youtube
, 153, 226–227
Zalando (companies)
, 230
- Prelims
- Chapter 1: Artificial Intelligence for Recruitment and Selection
- Chapter 2: Acceptance of Artificial Intelligence in Human Resource Practices by Employees
- Chapter 3: Technology and the Change in the Nature of Work. The Impact on HRM Practices: The Protean Career and Boundaryless Career
- Chapter 4: Challenges of Adopting AI Technology With Special Reference to HR Practices and Employees’ Acceptability and Accountability
- Chapter 5: Adoption of Artificial Intelligence in HR Practices: An Empirical Analysis
- Chapter 6: An Analysis of the Challenges to Human Resource in Implementing Artificial Intelligence
- Chapter 7: Challenges of Adopting Artificial Technology in Human Resource Management Practices
- Chapter 8: Artificial Intelligence and Robotic Automation Hit by the Pandemic: Reality or Myth
- Chapter 9: Deciphering the Role of Artificial Intelligence in Health Care, Learning and Development
- Chapter 10: Reskilling Workforce for the Artificial Intelligence Age: Challenges and the Way Forward
- Chapter 11: HRM Digitalisation: Exploring Success and Outcomes
- Chapter 12: Role of Artificial Intelligence in Re-inventing Human Resource Management
- Chapter 13: A Literature Review on HR Analytics: Trends and Future Challenges
- Index