Absorption

, 103–104

Ailments

, 31–32

Alaska Permanent Fund

, 153

Algorithms

, 81–82, 89–90, 92–93, 110, 113, 148

Amazon

, 32–33, 56–58

Amazon Web Services

, 34–35

Army Air Corps missions

, 51–52

Artificial intelligence

, 21–22, 26–27, 29, 31

age of

, 45

applications

, 31–34, 63–65

capabilities

, 63

future of

, 29

narrow forms

, 42–43

work of

, 32–33

Automatic teller machines (ATMs)

, 29–31

Automation

, 21–22, 26–27, 29, 31, 71

age of

, 45

future of

, 29

Big data

, 125, 142–143

Big Short: Inside the Doomsday Machine and Boomerang: Travels in the New Third World, The (Lewis)

, 75–76

Burnout

, 11–12, 106–107

Capitalist economic system

, 25–26

Care

, 129

services

, 120–121

workers

, 148

Change

, 13–14, 88

Chatbots

, 31–34, 71–72, 82–83, 92–94

China’s “one child” policy

, 138–139

China’s manufacturing sector

, 26–27

Chronic burnout

, 11–12

Civilization

, 15–16

Classrooms

, 55–56

Climate

, 86–87

Clock time

, 103

Codebreaking machines (Turing)

, 31

Companies

, 87–88, 91–92, 118, 120, 138

missions and goals

, 45–46

staffing practices

, 46–47

Compensation

, 48

Construction industry

, 36

Consultants

, 118–120

Contract employment

, 77–78

Coping

, 106–107

Correspondence courses

, 55–56

COVID-19

pandemic

, 71–72, 89, 120

recession

, 73, 76–77, 89

Creativity

, 160

Culture

, 86–87

Cyber Valley

, 94

Data

, 110, 113, 149

Dedication

, 103–104

Deep learning

, 38

Direct compensation

, 48

Dot.Com

era

, 56–57

recession

, 117–118

Education(al)

, 124–125, 129–130

professionals

, 120

systems

, 127–128

Emotional exhaustion

, 106–107

Employees

, 86–88, 92, 107–108

development functions

, 47–48

engagement

, 103–104

Employment. See also Work

, 23

data to government agencies

, 52

law

, 48–49

Enterprise resource planning systems

, 90

Entitlements

, 23–24

Entrepreneurs

, 18

Equity theory

, 23–24

Erewhon (Butler)

, 31

Etruscan civilization

, 15–16

Facebook

, 32–33, 56, 58

Fairness

, 23–24

Family business

, 18–19

Federal government

, 90

Ferris Bueller’s Day Off (movie)

, 70

“Fill-in-the-oval” response forms

, 52–53

Fog of War, The (Morris’s documentary film)

, 51–52

Fourth Industrial Revolution

, 13–14, 26–28, 45, 74, 79, 82–83, 85, 89–90, 111–112, 115–116, 144–145

conditions

, 102

development, implementation, and adoption

, 131–132

effects

, 131–132

fundamental paradox

, 97

massive overhaul in response to

, 94

paradox

, 129–130

technologies

, 101, 116, 142

Frankenstein (Shelley)

, 31

Free-market capitalist economic system

, 26

Freedom Dividend

, 155–156

Georgia Institute of Technology

, 94

Gig Economy

, 77–78, 121

Gig employees

, 151

Gig worker

, 121

Gig workforce

, 129, 157

Global supply chains

, 137–138

GLOBE study

, 17–18

Goods, production of

, 16–17

Google

, 56–57

recruitment practices

, 50–51

refinements of self-driving cars

, 32–35

Governments

, 25, 89–92, 118, 120, 152–153

Graduate Management Admission Test (GMAT)

, 60–61

Graduate Record Examinations (GRE)

, 60–61

Great Depression

, 73

Great Recession (2008)

, 72–73, 117–118, 156

Guns, Germs, and Steel (Diamond)

, 132–133

Hard work

, 6–7

Hasbro

, 138–140

Healthcare industry

, 36

Higher education

, 96–97

Hofstede’s national culture dimensions

, 17–18

Hologram technology

, 92–93, 95

Homo erectus

, 7

Homo sapiens

, 7

Horizontal strategic HRM integration

, 49–50

Human capital

, 45–46

Human resources

challenges for

, 130

functions

, 54

professionals

, 83–84, 108–110, 126–127, 133–134

Human resources management (HRM)

, 45

practices and processes

, 45–46

traditional functions

, 46–47, 49–50

Humans

, 3–5, 8–9, 121–122

Idiocracy (Mike Judge film)

, 149–150

@iLabAfrica

, 94

Indirect compensation

, 48

Innovation

, 10–11, 160–161

Inquiry into the Nature and Causes of the Wealth of Nations, An (Smith)

, 16–17

International Business Machines (IBM)

, 51–52

International staffing strategies

, 136–137

Internet

, 54

power of

, 55

search histories

, 148

Internet of Things

, 70–71, 81–82

Japan’s automotive manufacturing sector

, 134

Job(s). See also Employees

, 35–38, 86, 115–116

analysis

, 49–50

applicants

, 49–50, 79–80

description

, 49–50, 59–60

incumbent surveys

, 52–53

loss

, 74–75

specification

, 59–60

Killer robots

, 94

Kiosks

, 109–110

Knowledge Economy

, 136–137

Labor law

, 23

Linear time

, 103–104

LinkedIn

, 56, 58, 61–62, 80–81

Linking

, 92–93

Love Boat, The (television series)

, 121–122

Luck

, 132–133

Lyft

, 71–72

Machine learning

, 21–22, 26–27, 29

age of

, 45

applications

, 38

future of

, 29

and robotics

, 41

Machinery of shop floors

, 53–54

Machines

, 39–40, 97–98, 116–117

codebreaking

, 31

robo-caller

, 26–27

smart

, 162–163

Marshall Plan

, 24–25

Matrix, The

, 33, 149–150

Means testing

, 157–158

Metropolis (Lang’s German film)

, 31

Microsoft Teams

, 120

Military forces

, 29–31

Minority Report

, 33

Mixed-market economy

, 26

Money

, 2–3

National culture

, 17–18

National laws

, 22

Neural networks

, 38

Non–family-owned businesses or corporations

, 20

Offshoring strategies

, 136–137

Opportunity

, 132–133

Organizations

, 87–88

Outsourcing strategies

, 136–137

Pandora

, 60–61

Patrons

, 98

People

, 86–87

Performance management

, 47–48, 111

Personnel function

, 133–134

Political economic systems

, 25–26

Position Analysis Questionnaire

, 52–53

Poverty

, 74–75

Protestant Work Ethic

, 6–7

Psychological time

, 103–104

Purchasing information

, 123–124

Recruiters

, 61–62, 113–114

Recruiting

, 46–47, 59–60

Reduced self-efficacy

, 106–107

Resources

, 104–106

Robotics

, 39–41

Robots

, 29, 31, 36, 39–41, 53–54, 71–72

artificially intelligent

, 43–44

effectiveness of

, 70

Rogue One (film)

, 92–93

Roman civilization

, 15–16

Science, technology, engineering, and math disciplines (STEM disciplines)

, 96–97

Selection

, 63–64

Self-checkout kiosks

, 71–72

Self-driving cars

, 71–72

Self-efficacy

, 10–11

Self-help technologies

, 109–110

Sense of self and belonging

, 3–4

Siemens

, 135–136

Singular artificial intelligence

, 42–43

Siri

, 31–32

Smartphone

, 123–124

Social identity process

, 5

Social media

, 123–124

Social Network, The

, 56

Social safety nets

, 23–24

Social Security

, 152–153

Socialist economic system

, 25–26

Sports teams

, 5

Spotify

, 60–61

Staffing scenarios

, 60

State’s flagship higher education system

, 153

Stress

, 104–105

daily cycle

, 106–107

work

, 10

Stressors

, 27, 104–105

Succession

, 107–108

Sweden’s automotive industry

, 26

Technical jobs

, 96

Technology

, 85, 95, 104, 111, 120, 147–148

Telemarketing jobs

, 21–22, 26–27

Temporary employment

, 77–78

Terminator, The

, 33, 149–150

Terminator-style future

, 144–145

Tesla refinements of self-driving cars

, 32–35

Tests

, 46–47, 64–65

Thinking, Fast and Slow (Kahneman)

, 125–126

Three-year experimental UBI program

, 153–154

Total Recall

, 149–150

Trade unions and guilds

, 21–22

Training

, 47–48, 92–93

Twitter

, 32–33, 60–61

Uber

, 71–72

Universal basic income (UBI)

, 152–153, 157–158, 161

University of Houston in Texas

, 55–56

University of Sydney

, 94

Vertical human resource integration

, 50–51

Vigor

, 103–104

Virtual management assistants

, 113

Virtual reality programs

, 113

Wahaha Group in China

, 18–19

War Games

, 33

WhatsApp

, 58

Withdrawal

, 106–107

Work

, 1–2, 9, 24–25

analysis

, 49–50

of artificial intelligence

, 32–33

centrality

, 21–22

hard

, 6–7

human activity

, 16

identification

, 7

importance

, 89

loss of

, 11–12

person to

, 3–4

stress

, 10

work–family conflict

, 18–19

Workforces

, 81, 100, 151

World War II

aftermath

, 24

companies

, 52

Zoom

, 120