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 MONTEMAYOR and Ana Valdés LOYOLA
2.1. Introduction 19
2.2. Literature review 20
2.3. Methodology 24
2.4. Findings 25
2.5. Discussion and conclusion 39
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 FERDIAN and Yudha Aditya FIANDRA
3.1. Introduction 47
3.2. What is AI? A practical overview 49
3.3. AI for smarter management practices 50
3.4. AI in engineering: enhancing efficiency without coding 53
3.5. Easy-to-use AI tools for non-technical professionals 56
3.6. Ethical and organizational considerations 58
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
4.3. Method 73
4.4. Results 75
4.5. Discussion and conceptual model 78
4.6. Conclusions 82
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
5.3. Research methodology 97
5.4. Analysis 105
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
5.9. 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
6.3. Methodology 146
6.4. Analysis of the results 148
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
7.3. Mechanisms of emotional nudging through AI 166
7.4. Applications of emotional nudging 171
7.5. Ethical and societal implications 176
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
8.3. AAI systems opportunities in marketing 193
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
9.3. 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
10.3. 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
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
11.3. Methodology 278
11.4. Analysis and discussion of results 282
11.5. Best practice guide for using AI in HRM 286
11.6. Conclusion 287
11.7. Declaration 289
11.8. References 289
List of Authors 293
Index 297
