Artificial Intelligence Technologies in Management and Engineering

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.

• Preface xiiiCarolina MACHADO and J. Paulo DAVIMChapter 1. From Algorithms to Applications: AI in Management and Engineering 1Hamed TAHERDOOST and Mitra MADANCHIAN1.1. Introduction 11.2. Foundations of artificial intelligence 21.3. AI in management 51.4. AI in engineering 71.5. Comparative taxonomy of AI applications 91.6. Challenges and limitations 101.7. Future directions 121.8. Conclusion 121.9. References 13Chapter 2. Generational Perspectives on AI (From Baby Boomers to Gen Z): Understanding, Perceived Usefulness, Motivation to Adopt and Risk Perception 19Flor MORTON, Teresa TREVIÑO-BENAVIDES, Daniel Javier de la Garza MONTEMAYOR and Ana Valdés LOYOLA2.1. Introduction 192.2. Literature review 202.3. Methodology 242.4. Findings 252.5. Discussion and conclusion 392.6. References 43Chapter 3. Smart Decisions: How AI Is Transforming Everyday Management and Engineering Practices 47Soha RAWAS, Cerine TAFRAN, Agariadne Dwinggo SAMALA, Feri FERDIAN and Yudha Aditya FIANDRA3.1. Introduction 473.2. What is AI? A practical overview 493.3. AI for smarter management practices 503.4. AI in engineering: enhancing efficiency without coding 533.5. Easy-to-use AI tools for non-technical professionals 563.6. Ethical and organizational considerations 583.7. Future outlook: embracing AI with confidence 593.8. Conclusion 603.9. Declaration 613.10. References 61Chapter 4. Integrating AI into Business Education: Bridging the Gap Between Disciplinary Knowledge and Business Performance 65Laura Esther Zapata CANTÚ and Martha Elena Moreno BARBOSA4.1. Introduction 654.2. AI in business practices and education 674.3. Method 734.4. Results 754.5. Discussion and conceptual model 784.6. Conclusions 824.7. Declaration 844.8. References 84Chapter 5. Holistic Management Quo Vadis? Designing Management Dispositive and Metamorphic Possibilities in the age of AI 89Patrick BARETTO and Qeis KAMRAN5.1. Introduction 895.2. Designing a dispositive of knowledge 915.3. Research methodology 975.4. Analysis 1055.5. Toward an epistemic dispositive framework 1205.6. The architecture of the epistemic dispositive 1225.7. Metamorphic possibilities of the management dispositive 1245.8. An apology for the management dispositive: a call for strategic foresight 1255.9. Declaration 1305.10. References 131Chapter 6. Mapping the Use of Generative AI in Spain's Advertising Sector: Current Trends and Future Challenges 135Juan Manuel Corbacho VALENCIA, Jesús Pérez SEOANE and Xabier MARTÍNEZ-ROLÁN6.1. Introduction 1366.2. Global perspectives on AI in advertising and creative processes 1376.3. Methodology 1466.4. Analysis of the results 1486.5. Conclusions 1536.6. References 154Chapter 7. Emotional Nudging in the Rise of Affective Artificial Intelligence 159Cristiana Cerqueira LEAL and Benilde OLIVEIRA7.1. Introduction: from nudging to AI-based emotional hypernudging 1597.2. Emotions and decision-making 1627.3. Mechanisms of emotional nudging through AI 1667.4. Applications of emotional nudging 1717.5. Ethical and societal implications 1767.6. Final remark: long-term impact on human behavior, trust and rationality 1807.7. Abbreviations 1817.8. Acknowledgments 1817.9. Declaration 1817.10. References 181Chapter 8. Agentic AI in Marketing: Opportunities, Challenges and Impact on Firm Performance 185Florin Sabin FOLTEAN and Octavian Dumitru HERA8.1. Introduction 1858.2. AAI systems 1868.3. AAI systems opportunities in marketing 1938.4. Challenges of AAI systems adoption in marketing organizations 1968.5. Business value of AAI systems in marketing 1988.6. Conclusion 1998.7. References 200Chapter 9. AI's Role in Marketing: Mapping the Evolution of Creativity 205Teresa TREVIÑO-BENAVIDES and Flor MORTON9.1. Introduction 2059.2. Literature review 2079.3. Challenges and limitations of AI in marketing 2169.4. Future directions of AI in marketing and creativity 2179.5. Implications and future research 2179.6. References 218Chapter 10. Unveiling Management Research's Thematic Evolution: An Unsupervised Machine Learning – Latent Dirichlet Allocation Perspective 223Qeis KAMRAN10.1. Introduction 22410.2. Method 22510.3. Analyses 24110.4. Results of the content analysis 24410.5. Contributing authors 24910.6. Most influential papers 24910.7. Box plotting 25010.8. Conclusion 25110.9. References 25210.10. Appendix 1. Application of the machine learning methodology to investigate the domain of entrepreneurship and marketing 256Chapter 11. The Use of AI in Human Resource Management: Barriers, Opportunities and Trends. 269Pedro Miguel Torres BARROS and Carolina MACHADO11.1. Introduction 27011.2. Theoretical framework 27111.3. Methodology 27811.4. Analysis and discussion of results 28211.5. Best practice guide for using AI in HRM 28611.6. Conclusion 28711.7. Declaration 28911.8. References 289List of Authors 293Index 297