Artificial intelligence (AI) technologies play a transformative role in several areas of knowledge, including management and engineering. Their adoption has been driven by the advancement of machine learning algorithms, increased computing power, and the availability of large volumes of data, making AI technologies indispensable for process optimization and strategic decision-making. However, organizations must invest in research, development and professional training to ensure AI is used ethically and sustainably to drive progress.
This book makes several contributions, by not only advancing scientific and technical knowledge, but also improving efficiency and decision-making, and developing new tools and technologies.
The main aim of Artificial Intelligence Technologies in Management and Engineering is to provide a channel for sharing and disseminating knowledge of new advances in AI technologies in management and engineering among academics/researchers, managers and engineers. It seeks to advance research in the field, provide practical insights for managers and engineers, and also serve as a basis for future technological innovations.
Table of Contents:
Preface xiii
Carolina MACHADO and J. Paulo DAVIM
Chapter 1 From Algorithms to Applications: AI in Management and Engineering 1
Hamed TAHERDOOST and Mitra MADANCHIAN
1.1 Introduction 1
1.2 Foundations of artificial intelligence 2
1.3 AI in management 5
1.4 AI in engineering 7
1.5 Comparative taxonomy of AI applications 9
1.6 Challenges and limitations 10
1.7 Future directions 12
1.8 Conclusion 12
1.9 References 13
Chapter 2 Generational Perspectives on AI (From Baby Boomers to Gen Z): Understanding, Perceived Usefulness, Motivation to Adopt and Risk Perception 19
Flor MORTON, Teresa TREVIÑO-BENAVIDES, Daniel Javier de la Garza MONTEMAYORand Ana Valdés LOYOLA
2.1 Introduction 19
2.2 Literature review 20
2.2.1 Perceived usefulness of artificial intelligence 20
2.2.2 Uses and gratifications theory and AI use 21
2.2.3 Perceived risk 23
2.3 Methodology 24
2.4 Findings 25
2.4.1 Baby Boomers 25
2.4.2 Generation X 28
2.4.3 Millennials 33
2.4.4 Centennials 37
2.5 Discussion and conclusion 39
2.5.1 Baby Boomers: cautious and curious 42
2.5.2 Generation X: pragmatic realism 42
2.5.3 Millennials: enthusiastic yet concerned 42
2.5.4 Centennials: acceptance with awareness 42
2.6 References 43
Chapter 3 Smart Decisions: How AI Is Transforming Everyday Management and Engineering Practices 47
Soha RAWAS, Cerine TAFRAN, Agariadne Dwinggo SAMALA, Feri FERDIANand Yudha Aditya FIANDRA
3.1 Introduction 47
3.2 What is AI? A practical overview 49
3.3 AI for smarter management practices 50
3.3.1 The expanding role of AI in management 50
3.3.2 Real-world tools and use cases 52
3.3.3 Strategic advantages of AI-augmented management 53
3.4 AI in engineering: enhancing efficiency without coding 53
3.4.1 How AI is reshaping engineering workflows 53
3.4.2 No-code AI tools for engineering use cases 55
3.4.3 Key advantages in engineering contexts 56
3.5 Easy-to-use AI tools for non-technical professionals 56
3.5.1 Key user-friendly AI platforms 57
3.5.2 Practical applications across roles 57
3.5.3 Advantages of no-code AI tools 57
3.6 Ethical and organizational considerations 58
3.6.1 Transparency and explainability 58
3.6.2 Bias and fairness 58
3.6.3 Data privacy and security 59
3.6.4 Organizational readiness and culture 59
3.6.5 Evaluating ethical AI tools 59
3.7 Future outlook: embracing AI with confidence 59
3.8 Conclusion 60
3.9 Declaration 61
3.10 References 61
Chapter 4 Integrating AI into Business Education: Bridging the Gap Between Disciplinary Knowledge and Business Performance 65
Laura Esther Zapata CANTÚ and Martha Elena Moreno BARBOSA
4.1 Introduction 65
4.2 AI in business practices and education 67
4.2.1 AI boosting the use of technology in workplaces 68
4.2.2 AI in business practices 69
4.2.3 AI as a driving force for business schools 70
4.2.4 AI in business education and curriculum alignment: disciplinary competencies 72
4.3 Method 73
4.4 Results 75
4.4.1 AI benefits in Mexican firms 75
4.4.2 AI risks in Mexican firms 76
4.4.3. Perception of competences requiring development in business schools 77
4.5 Discussion and conceptual model 78
4.5.1 How do business schools respond to industry needs in the context of AI integration? 79
4.5.2 How do business schools ensure competence development (soft skills and disciplinary competences) when incorporating AI? 80
4.6 Conclusions 82
4.6.1 Theoretical implications 82
4.6.2 Practical implications 83
4.6.3 Limitations 84
4.7 Declaration 84
4.8 References 84
Chapter 5 Holistic Management Quo Vadis? Designing Management Dispositive and Metamorphic Possibilities in the age of AI 89
Patrick BARETTO and Qeis KAMRAN
5.1 Introduction 89
5.2 Designing a dispositive of knowledge 91
5.2.1 Management as knowledge of practice 94
5.2.2. Management’s drift: from knowledge of practice to knowledge of tools 95
5.2.3 From tools to theorization: the inversion of technology and AI 96
5.3 Research methodology 97
5.3.1 LDA: topic modeling 99
5.3.2 Mapping topics to knowledge spheres 104
5.4 Analysis 105
5.4.1 Multiple correspondence analysis 105
5.4.2 Content analysis 106
5.4.3 Heatmap analysis 111
5.4.4 Meta-synthesis: reconfiguring the epistemic ecology of knowledge 117
5.5 Toward an epistemic dispositive framework 120
5.6 The architecture of the epistemic dispositive 122
5.7 Metamorphic possibilities of the management dispositive 124
5.8 An apology for the management dispositive: a call for strategic foresight 125
5.8.1 In defense of management as an epistemic domain 125
5.9 Declaration 130
5.10 References 131
Chapter 6 Mapping the Use of Generative AI in Spain’s Advertising Sector: Current Trends and Future Challenges 135
Juan Manuel Corbacho VALENCIA, Jesús Pérez SEOANE and Xabier MARTÍNEZ-ROLÁN
6.1 Introduction 136
6.2 Global perspectives on AI in advertising and creative processes 137
6.2.1 Spanish empirical research on GenAI adoption and professional practices 138
6.2.2 GenAI changing the creative process 141
6.2.3 Consumer response and cultural adaptation research 143
6.2.4 Ethical frameworks and regulatory compliance 143
6.2.5 Future research directions 144
6.3 Methodology 146
6.3.1 Identification of the sample 146
6.4 Analysis of the results 148
6.4.1 Perception of GenAI tools 148
6.4.2 Uses of GenAI in the professional environment 149
6.4.3 Advertisers and GenAI 151
6.4.4 Limitations and inhibitors 152
6.5 Conclusions 153
6.6 References 154
Chapter 7 Emotional Nudging in the Rise of Affective Artificial Intelligence 159
Cristiana Cerqueira LEAL and Benilde OLIVEIRA
7.1 Introduction: from nudging to AI-based emotional hypernudging 159
7.2 Emotions and decision-making 162
7.2.1 Human emotions: what is an emotion? 162
7.2.2 A catalogue of emotions for decision-making 163
7.3 Mechanisms of emotional nudging through AI 166
7.3.1 Human emotions versus synthetic emotion in AI 166
7.3.2 Emotion recognition: reading the room 167
7.3.3 Emotion expression/generation: shaping the stimulus 168
7.3.4 Emotional personalization in adaptive loops: what works for whom 169
7.3.5 Temporal and contextual variability 170
7.4 Applications of emotional nudging 171
7.4.1 Public policy and civic engagement 171
7.4.2 Health and well-being 171
7.4.3 Sustainability and climate action 172
7.4.4 Financial behavior 172
7.4.5 Education and learning environments 173
7.4.6 Digital platforms: social media, e-commerce, fintech 174
7.4.7 Personal assistants 175
7.5 Ethical and societal implications 176
7.5.1 Autonomy and manipulation 176
7.5.2 Bias, discrimination and fairness 177
7.5.3 Guiding principles for responsible emotional nudging 177
7.6 Final remark: long-term impact on human behavior, trust and rationality 180
7.7 Abbreviations 181
7.8 Acknowledgments 181
7.9 Declaration 181
7.10 References 181
Chapter 8 Agentic AI in Marketing: Opportunities, Challenges and Impact on Firm Performance 185
Florin Sabin FOLTEAN and Octavian Dumitru HERA
8.1 Introduction 185
8.2 AAI systems 186
8.2.1 AI agency concept 186
8.2.2 AI agents 188
8.2.3 Architecture and operational mechanisms of AAI systems 191
8.3 AAI systems opportunities in marketing 193
8.3.1 Applications of AAI systems in marketing processes 193
8.3.2 Architecture of AAI systems in marketing 194
8.4 Challenges of AAI systems adoption in marketing organizations 196
8.5 Business value of AAI systems in marketing 198
8.6 Conclusion 199
8.7 References 200
Chapter 9. AI’s Role in Marketing: Mapping the Evolution of Creativity 205
Teresa TREVIÑO-BENAVIDES and Flor MORTON
9.1 Introduction 205
9.2 Literature review 207
9.2.1 Creativity and creative thinking in organizations 207
9.2.2 Phase 1 mechanical AI: marketing automation and analytics 209
9.2.3 Phase 2 GenAI: creativity enhancement and personalization 210
9.2.4 The effects of personalization on consumer experience and behavior 212
9.2.5 AI-driven personalization and consumer engagement 213
9.2.6 Phase 3 collaborative AI: a tool for innovation 214
9.3 Challenges and limitations of AI in marketing 216
9.4 Future directions of AI in marketing and creativity 217
9.5 Implications and future research 217
9.6 References 218
Chapter 10 Unveiling Management Research’s Thematic Evolution: An Unsupervised Machine Learning – Latent Dirichlet Allocation Perspective 223
Qeis KAMRAN
10.1 Introduction 224
10.2 Method 225
10.2.1 Pre-processing 228
10.2.2 Topic detection via LDA 232
10.2.3 Post LDA 239
10.2.4 Limitation of the LDA methodology and comments 240
10.3 Analyses 241
10.4 Results of the content analysis 244
10.5 Contributing authors 249
10.6 Most influential papers 249
10.7 Box plotting 250
10.8 Conclusion 251
10.9 References 252
10.10 Appendix 1 Application of the machine learning methodology to investigate the domain of entrepreneurship and marketing 256
10.10.1 Introduction 256
10.10.2 Method 257
10.10.3 CA – the most cited articles per journal per year 259
10.10.4 Conclusion 265
10.10.5 References 267
Chapter 11 The Use of AI in Human Resource Management: Barriers, Opportunities and Trends 269
Pedro Miguel Torres BARROS and Carolina MACHADO
11.1 Introduction 270
11.2 Theoretical framework 271
11.2.1 Hrm 272
11.2.2 Ai 274
11.2.3 Integrating AI into HRM 277
11.3 Methodology 278
11.3.1 Sample selection and participants 278
11.3.2 Data collection 279
11.3.3 Data analysis 279
11.3.4 Sample characterization 279
11.4 Analysis and discussion of results 282
11.4.1 Adoption of AI in HRM 282
11.4.2 Perceptions about AI in HRM 283
11.4.3 Areas of application and/or non-application of AI in HRM 285
11.4.4 Future perspectives of AI in HRM 285
11.5 Best practice guide for using AI in HRM 286
11.5.1 Fundamental principles 286
11.5.2 Applications and best practices 286
11.5.3 Challenges and solutions 287
11.5.4 Gradual and sustainable implementation 287
11.6 Conclusion 287
11.7 Declaration 289
11.8 References 289
List of Authors 293
Index 297
About the Author :
Carolina Machado is an associate professor with habilitation at the University of Minho, Portugal. She has lectured on HRM subjects since 1989. She is currently the Head of the HRM Work Group at the University of Minho and is also the Editor-in-Chief of the International Journal of Applied Management Sciences and Engineering.
J. Paulo Davim is a professor at the University of Aveiro, Portugal and is also distinguished as an honorary professor in several universities/colleges/institutes in China, India and Spain. He has more than 35 years of teaching and research experience in mechanical and industrial engineering.
