Index
Lerato Aghimien
(University of Johannesburg, South Africa)
Clinton Ohis Aigbavboa
(University of Johannesburg, South Africa)
Douglas Aghimien
(De Montfort University, United Kingdom/University of Johannesburg, South Africa)
Construction Workforce Management in the Fourth Industrial Revolution Era
ISBN: 978-1-83797-019-3, eISBN: 978-1-83797-018-6
Publication date: 12 February 2024
This content is currently only available as a PDF
Citation
Aghimien, L., Aigbavboa, C.O. and Aghimien, D. (2024), "Index", Construction Workforce Management in the Fourth Industrial Revolution Era, Emerald Publishing Limited, Leeds, pp. 207-212. https://doi.org/10.1108/978-1-83797-018-620241009
Publisher
:Emerald Publishing Limited
Copyright © 2024 Lerato Aghimien, Clinton Ohis Aigbavboa and Douglas Aghimien
INDEX
Ability model
, 108–109
Ability–motivation–opportunity theory
, 86
Accuracy in compensation
, 59
AFROSAI-E
, 135
American Society for Training and Development (ASTD)
, 96
Artificial intelligence (AI)
, 43, 45, 51–52
(see also Emotional intelligence (EI))
Assessment tools
, 137
Automation
, 54–55
Bar-on emotional intelligence competencies model
, 108–109
Behavioural perspective
, 87
Big data analytics
, 42, 52–53, 202
Blockchain technology
, 53–54
Blue-collar workers
, 14
Building information modelling (BIM)
, 119, 146
Classical approach
, 87
Cloud computing
, 42, 49–50, 202
Coercive pressure
, 117
Cognitive intelligence
, 106
Compensation
and benefits
, 132–135, 169–177, 195
improved accuracy in
, 59
packages
, 133
process
, 203
Conceptual/conceptualised construction workforce management model
, 127, 160, 166, 206
constituents of Delphi
, 160–161
Delphi outcomes
, 165–197
using Delphi to explore applicability
, 160
design and execution of Delphi
, 161–165
theorising
, 148–151
Conceptualised digitalization
, 44
Conceptualised model
, 201, 204
Configuration theory
, 85
Construction
, 12, 104, 114, 131, 146
Fourth Industrial revolution and
, 42–44
industry application of motivation theories in
, 82–83
projects
, 26
sector
, 14
workers
, 6, 15–16, 26, 75, 82, 113
workforce management in
, 3–5
Construction industry
, 1–3, 11–13, 50, 104, 134, 140, 143, 197, 202
challenges facing effective workforce management in construction industry
, 25–28
constraints of
, 13–16
improving effectiveness of workforce management in construction industry
, 28–29
research focus in construction workforce management
, 21–25
workforce management in
, 20
Construction organizations
, 5–6, 92, 137, 151, 203, 205
in deploying digital technologies for effective workforce management, envisaged challenges for
, 60–63
Construction Sector Council (CSC)
, 27
Construction workforce management
compensation and benefits
, 132–135
digital technologies for effective
, 49–56
EI
, 104–105
emotional intelligence in
, 113–114, 144–145
employee involvement and empowerment
, 142–144
expected outcomes of effective construction workforce management
, 147–148
external environment
, 115–116, 119, 146–147
gaps in
, 104
institutional theory
, 116–119
main constructs of
, 166–167
measurement variables of
, 167–191
model
, 75
performance appraisal and management
, 135–138
recruitment and selection
, 128–132
technological advancement in
, 44–46
theories of emotional intelligence
, 108–113
theorising conceptualised workforce management model
, 148–151
training and development
, 138–142
understanding concept of emotional intelligence
, 105–108
variable selection for construction workforce management
, 128
Content theories
, 76
ERG theory
, 77
Herzberg’s two-factor theory
, 78–79
Maslow’s hierarchy of Needs
, 76–77
McClelland’s needs motivation theory
, 77–78
Contingency theory
, 84–85
Contribution to knowledge
, 204
practical contribution
, 205
theoretical contribution
, 204
Conventional methods
, 56
Coronavirus (COVID-19)
, 2
Cronbach alpha test
, 167
Cultural context
, 117
Cyclic process
, 89
Data management
, 57
Data-driven technologies
, 42
Decision-making process
, 142
Delphi constituents
, 160–161
Delphi design and execution
, 161
computing data and determining consensus from Delphi
, 164–165
conducting Delphi iterations
, 163–164
criteria for expert panel selection
, 161–163
Delphi outcomes
, 165
main constructs of construction workforce management model
, 166–167
measurement variables of construction workforce management model
, 167–191
overall view of workforce management practices
, 191–194
Delphi process
, 159–160
Delphi study
, 162–164
Delphi to explore applicability of conceptualised workforce management model
, 160
Descriptive theories
, 87
Digital human resource management
, 45
Digital platforms
, 144
Digital technologies
, 17, 44, 55, 58, 146–147, 202
AI
, 51–52
big data analytics
, 52–53
blockchain technology
, 53–54
cloud computing
, 49–50
cost savings
, 58
for effective construction workforce management
, 49
efficiency in performance management
, 59
enhanced trust
, 59–60
improved accuracy in compensation
, 59
improved communication
, 58
improved data management
, 57
improved occupational health and safety
, 60
improved productivity
, 57
improved training
, 56–57
increased employee engagement and participation
, 58
IoT
, 51
key opportunities in digital technologies deployment for construction workforce management
, 56
mobile applications
, 55–56
robotics and automation
, 58–59
smart recruitment
, 56
strategic decision-making and problem-solving
, 58–59
Digital tools
, 56
in workforce management
, 58
Digital transformation
, 44, 46
capability perspective of
, 47
Digital workforce management
, 61
Digitalisation
, 41–45
Direct compensation
, 133
E-communication
, 58
E-recruitment
, 56, 130
Electronic human resource management
, 45
Electronic platforms
, 203
Emerging digital tools
, 58
Emerging technologies
, 60
Emotional intelligence (EI)
, 4, 104–105, 127, 144–145, 183–190, 203
ability model
, 109
bar-on emotional intelligence competencies model
, 109
in construction workforce management
, 113–114
Goleman’s mixed model of emotional intelligence
, 109–113
theories of
, 108
understanding concept of
, 105–108
Empathy
, 112
Employee
, 78
engagement and participation
, 58
involvement and empowerment
, 142–144, 183
productivity
, 57
Employment practices
, 17
Equity Theory
, 80–81
Evaluation process
, 196
Evolutionary approach
, 87
Existence, Relatedness, and Growth (ERG)
, 76–77
Expectancy
, 80
theory
, 79–80
Expert panel selection, criteria for
, 161–163
Exponential technology
, 45
External environment
, 115–116, 146–147, 191, 197
construct
, 146
influence on construction workforce management
, 119
Fourth Industrial Revolution
, 41, 43, 130, 202, 205
bibliometric perspective of workforce management and Fourth Industrial Revolution
, 46–49
and construction
, 42–44
envisaged challenges for construction organisations in deploying digital technologies for effective workforce management
, 60–63
era
, 4, 17, 137, 143
key opportunities in digital technologies deployment for construction workforce management
, 56–60
overview of technological advancement in construction workforce management
, 44–46
types of digital technologies for effective construction workforce management
, 49–56
Frustration-regression
, 77
Goal-setting theory
, 81–82
Goleman’s Mixed Model of Emotional Intelligence
, 109, 197
empathy
, 112
motivation
, 111–112
self-awareness
, 110–111
self-regulation/management
, 111
social skills
, 112–113
Government legislation
, 119
Green workforce management
, 19
Gross domestic product (GDP)
, 13
Guest Model
, 90–91, 115
Hard and Soft Model
, 91–92
Harvard framework
, 88
Harvard Model
, 88–89, 115
Herzberg’s theory
, 82
Herzberg’s two-factor theory
, 78–79
High-powered computing technology
, 45
Human psychology
, 203
Human resources
, 2
analytic systems
, 137
management
, 2, 16, 89, 162
Humanistic psychology
, 107
Hygiene factors
, 78
Industry 4.0 (see Fourth Industrial Revolution)
Inequity
, 80
Infrastructure as a Service (IaaS)
, 50
Innovation
, 43, 58
Inputs
, 80
Institutional theory
, 116–119
Instrumentality
, 80
Intellectual capital
, 139
Intelligence quotient (IQ)
, 105
International Labour Organisation (ILO)
, 2
Internet
, 130
Internet of Things (IoT)
, 43, 51, 119
Interquartile deviation (IQD)
, 164
Intranet
, 130
Job analysis
, 129
Kick in the Ass approach (KITA approach)
, 82
Knowledge, contribution to
, 204–205
Labour-intensive industry
, 2
LinkedIn
, 195
Machine learning (ML)
, 45
Macro-environmental factors
, 115
Management strategy
, 93
Mann–Whitney U-test (M–W test)
, 167–168, 190
Manufacturing process
, 43
Maslow’s hierarchy of needs
, 76–77
Maslow’s theory
, 82
Matching Model
, 89–90
McClelland’s Needs Motivation Theory
, 77–78
McGregor’s X theory
, 91
McGregor’s Y theory
, 91
Measurement variables of construction workforce management model
, 167
compensation and benefits
, 169–177
emotional intelligence
, 183–190
employee involvement and empowerment
, 183
external environment
, 191
performance management and appraisal
, 177–179
recruitment and selection
, 167–169
training and development
, 179–183
Michigan model (see Matching model)
Mimetic pressure
, 118
Mobile applications
, 55–56
Motivation
, 111–112, 129
application of motivation theories in construction industry
, 82–83
of employees
, 21
in workforce management
, 76–83
Motivational factors
, 79
Motivational techniques
, 82
Network visualization
, 25
Normative pressure
, 118
Normative theories
, 87
Occupational health and safety
, 60
Office of National Statistics (ONS)
, 15
Online meeting platforms
, 57
Online social media platforms
, 195
Organisations
, 59, 118, 129–130
digital tools
, 58
workforce
, 2
Others-inside
, 81
Others-outside
, 81
Outputs
, 80
Over-reward
, 80
Performance appraisal and management
, 135–138
Performance management
, 135
and appraisal
, 177–179, 196
efficiency in
, 59
Physical technologies
, 55
Platform as a Service (PaaS)
, 50
Practical contribution
, 205
Pressure
, 116–117
Price Waterhouse Coopers, The (PwC)
, 60
Problem-solving
, 58–59
Process theories
, 79
equity theory
, 80–81
expectancy theory
, 79–80
goal-setting theory
, 81–82
Processual approach
, 87
Project-oriented organizations
, 4
Qualitative research approach
, 160
Radio frequency identification (RFID)
, 51
Recruitment
, 128–132, 167–169
process
, 195
Resource-based theory
, 85–86
Resource-based view (RBV)
, 85
Reviewed models
, 93
Robotics
, 54–55
Safety Information Modelling
, 55
Satisfaction-progression
, 77
Selection
, 128–132, 167–169, 195
Self-awareness
, 110–111
Self-inside
, 81
Self-outside
, 81
Self-regulation/management
, 111
Semi-Automated Mason
, 55
Smart recruitment
, 56
Social awareness
, 112
Social exchange theory
, 87
Social skills
, 112–113
Software as a Service (SaaS)
, 50
Standard deviation (SD)
, 165
Statistical Package for Social Sciences (SPSS)
, 164
Strategic decision-making
, 58–59
Strategic fit
, 87
Strategic theory
, 86–87
Structural Equation Modelling
, 21
Systemic approach
, 87
Talent management tools
, 137
Technology
, 57
implementation
, 62
Theoretical contribution
, 204
Three-dimensional printing
, 55
Training and development
, 138–142, 179–183
Trust
, 59–60
Under-reward
, 80
United States of America (USA)
, 15
Universalistic theory
, 83–84
Unmanned Aerial Vehicles (UAVs)
, 43, 55
Valence
, 80
Visualisation of Similarities Viewer (VOSviewer)
, 22–23, 47
Warwick Model
, 90, 115
Web of Science
, 21
Workers
, 104
Workforce management
, 2–3, 50
ability–motivation–opportunity theory
, 86
application of motivation theories in construction industry
, 82–83
bibliometric perspective of workforce management and Fourth Industrial Revolution
, 46–49
challenges facing effective workforce management in construction industry
, 25–28
configuration theory
, 85
in construction
, 3–5
in construction industry
, 20–25
content theories
, 76–79
contingency theory
, 84–85
and determining existing practices
, 92–96
envisaged challenges for construction organisations in deploying digital technologies for effective
, 60–63
Guest Model
, 90–91
Hard and Soft Model
, 91–92
Harvard Model
, 88–89
improving effectiveness of workforce management in construction industry
, 28–29
matching model
, 89–90
models
, 91, 128
models, and practices
, 201
motivation in
, 76
overall view of
, 191–194
overview of workforce management practices
, 17–19
practices
, 4, 19, 93, 117, 143, 147–148, 195
process
, 104
process theories
, 79–82
related theories
, 87–88
resource-based theory
, 85–86
strategic theory
, 86–87
system
, 61
theories and models
, 83
understanding
, 16
universalistic theory
, 83–84
Warwick Model
, 90
Workforce performance management suite systems
, 137
Working memory
, 111
World Economic Forum, The
, 13
- Prelims
- Chapter 1: General Introduction
- Chapter 2: Construction Workforce Management
- Chapter 3: Construction Workforce Management in the Fourth Industrial Revolution
- Chapter 4: Workforce Management Theories, Models, and Practices
- Chapter 5: Gaps in Construction Workforce Management Research
- Chapter 6: Conceptualising Construction Workforce Management
- Chapter 7: Exploring the Conceptualised Construction Workforce Management Model Through Experts' Opinion
- Chapter 8: Conclusion
- Index