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Artificial Intelligence: Technologies, Applications, and Policy Perspectives. Insights from Portugal

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Decision Intelligence Analytics and the Implementation of Strategic Business Management

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Abstract

Artificial Intelligence (AI) reshapes the global scenario and redefines development and service demand. AI stands as a disruptive technology that leads to numerous, more efficient activities, industrial processes, and new business models. The literature underlines that AI can be used in all aspects of organizations and individuals’ personal lives, and such nuances and potentials are still mostly unstudied, representing an interesting research gap. This chapter aims to emphasize the technologies and applications of AI to industries and services. Focusing then on the Portuguese context, this research’s main objective is to concentrate on AI to recognize state of the art and the main developments and trends in the industry and services and provide insights into AI policies.

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References

  1. C. Bagnoli, F. Dal Mas, M. Massaro, The 4th industrial revolution: Business models and evidence from the field. Int J E-Services Mob Appl. 11(3), 34–47 (2019)

    Google Scholar 

  2. C. Nielsen, M. Lund, M. Montemari, F. Paolone, M. Massaro, J. Dumay, Business Models: A Research Overview (Routledge, New York, 2018)

    Google Scholar 

  3. C. Bagnoli, M. Massaro, F. Dal Mas, M. Demartini, Defining the concept of business model: Searching for a business model framework. International Journal of Knowledge and System Science 9(3), 48–64 (2018). https://doi.org/10.4018/IJKSS.2018070104

    Google Scholar 

  4. M.J. Sousa, R. Cruz, Á. Rocha, M. Sousa, Innovation trends for smart factories: A literature review, in New Knowledge in Information Systems and Technologies WorldCIST’19 2019 Advances in Intelligent Systems and Computing, ed. by Á. Rocha, H. Adeli, L. Reis, S. Costanzo, (Springer, Cham, 2019), pp. 689–698

    Google Scholar 

  5. M.J. Sousa, A. Pesqueira, C. Lemos, M. Sousa, A. Rocha, Decision-making based on big data analytics for people Management in Healthcare Organizations. J. Med. Syst. 43(9), 290 (2019)

    Google Scholar 

  6. M.J. Sousa, A. de Bem Machado, Blockchain technology reshaping education contributions for policy, in Blockchain Technology Applications in Education, ed. by R. C. Sharma, H. Yildirim, G. Kurubacak, (IGI Global, Hershey, PA, 2020), pp. 113–125

    Google Scholar 

  7. A. Pesqueira, M. Sousa, A. Rocha, Big data skills sustainable development in healthcare and pharmaceuticals. J. Med. Syst. 44, 197 (2020)

    Google Scholar 

  8. K. Toniolo, E. Masiero, M. Massaro, C. Bagnoli, Sustainable business models and artificial intelligence. Opportunities and challenges, in Knowledge, People, and Digital Transformation: Approaches for a Sustainable Future, ed. by F. Matos, V. Vairinhos, I. Salavisa, L. Edvinsson, M. Massaro, (Springer, Cham, 2019), pp. 103–117

    Google Scholar 

  9. M.J. Sousa, F. Dal Mas, A. Pesqueira, C. Lemos, J.M. Verde, L. Cobianchi, The potential of AI in health higher education to increase the students’ learning outcomes. TEM Journal 10(2), 488–497 (2021). https://doi.org/10.18421/TEM102-02

    Article  Google Scholar 

  10. P. Wang, On defining Artificial Intelligence. J. Artif. Gen. Intell. 10(2), 1–37 (2019)

    Google Scholar 

  11. J. Schmidhuber, Deep learning in neural networks: An overview. Neural Netw. 61, 85–117 (2015)

    Google Scholar 

  12. W.J. Murdoch, C. Singh, K. Kumbier, R. Abbasi-Asl, B. Yu, Definitions, methods, and applications in interpretable machine learning. Proc. Natl. Acad. Sci. U. S. A. 116(44), 22071–22080 (2019)

    MathSciNet  MATH  Google Scholar 

  13. S. Bird, E. Klein, E. Loper, Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit (Sebastopol, O’Reilly Media, Inc, 2009)

    MATH  Google Scholar 

  14. D. Adebanjo, P.L. Teh, P.K. Ahmed, The impact of supply chain relationships and integration on innovative capabilities and manufacturing performance: The perspective of rapidly developing countries. Int J Prod Res 56(4), 170 (2018). https://doi.org/10.1080/00207543.2017.1366083

    Article  Google Scholar 

  15. H. Harlow, Developing a knowledge management strategy for data analytics and intellectual capital. Meditari. Account. Res. 26(3), 400–419 (2018). https://doi.org/10.1108/MEDAR-09-2017-0217

    Article  Google Scholar 

  16. S.L. Wamba-Taguimdje, S. Fosso Wamba, R. Kala Kamdjoug Jean, C.E. Tchatchouang Wanko, Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects. Bus. Process. Manage. J. 26(7), 189 (2020). https://doi.org/10.1108/BPMJ-10-2019-0411

    Article  Google Scholar 

  17. C. Bagnoli, A. Bravin, M. Massaro, A. Vignotto, Business Model 4.0 (Venezia, Edizioni Ca’ Foscari, 2018)

    Google Scholar 

  18. F. Dal Mas, M. Massaro, J.M. Verde, L. Cobianchi, Can the blockchain lead to new sustainable business models? J Bus Model. 8(2), 31–38 (2020)

    Google Scholar 

  19. F. Dal Mas, G. Dicuonzo, M. Massaro, V. Dell’Atti, Smart contracts to enable sustainable business models. A case study. Manag. Decis. 58(8), 1601–1619 (2020)

    Google Scholar 

  20. M. Del Giudice, Discovering the Internet of things (IoT) within the business process management: a literature review on technological revitalization. Bus. Process. Manag. J. 22(2), 263–270 (2016)

    Google Scholar 

  21. M. Massaro, S. Secinaro, F. Dal Mas, V. Brescia, D. Calandra, Industry 4. 0 and circular economy: An exploratory analysis of academic and practitioners’ perspectives. Bus. Strateg. Environ. 30(2), 1213–1231 (2021)

    Google Scholar 

  22. G. Briganti, O. Le Moine, Artificial intelligence in medicine: Today and tomorrow. Front. Med. 7, 1–6 (2020)

    Google Scholar 

  23. M.H. Stanfill, D.T. Marc, Health information management: Implications of Artificial Intelligence on healthcare data and information management. Yearb. Med. Inform. 28(1), 56–64 (2019)

    Google Scholar 

  24. C. Paton, S. Kobayashi, An Open Science approach to Artificial Intelligence in healthcare. Yearb. Med. Inform. 28(1), 47–51 (2019)

    Google Scholar 

  25. B.H. Li, B.C. Hou, W.T. Yu, X.B. Lu, C.W. Yang, Applications of artificial intelligence in intelligent manufacturing: a review. Front Inf Technol Electron Eng. 18(1), 86–96 (2017)

    Google Scholar 

  26. M. Massaro, J.C. Dumay, J. Guthrie, On the shoulders of giants: Undertaking a structured literature review in accounting. Accounting, Audit Account J. 29(5), 767–901 (2016)

    Google Scholar 

  27. A.C. Edmondson, S.E. McManus, Methodological fit in management field research. Acad. Manage. Rev. 32(4), 1155–1179 (2007)

    Google Scholar 

  28. A.L. Beam, I.S. Kohane, Big data and machine learning in health care. JAMA 319(13), 1317–1318 (2018)

    Google Scholar 

  29. F. Dal Mas, D. Piccolo, L. Cobianchi, L. Edvinsson, G. Presch, M. Massaro et al., The effects of artificial intelligence, robotics, and industry 4.0 technologies. insights from the healthcare sector, in: Proceedings of the first European Conference on the impact of Artificial Intelligence and Robotics. Academic Conferences and Publishing International Limited, 2019, pp. 88–95

    Google Scholar 

  30. A. Becker, Artificial intelligence in medicine: What is it doing for us today? Heal. Pol. Technol. 8(2), 198–205 (2019)

    Google Scholar 

  31. M.P. McBee, O.A. Awan, A.T. Colucci, C.W. Ghobadi, N. Kadom, A.P. Kansagra, et al., Deep learning in radiology. Acad. Radiol. 25(11), 1472–1480 (2018). https://doi.org/10.1016/j.acra.2018.02.018

    Article  Google Scholar 

  32. R.C. Mayo, J. Leung, Artificial intelligence and deep learning – Radiology’s next frontier? Clin. Imaging 49, 87–88 (2018)

    Google Scholar 

  33. R. Kapoor, S.P. Walters, L.A. Al-Aswad, The current state of artificial intelligence in ophthalmology. Surv. Ophthalmol. 64(2), 233–240 (2019)

    Google Scholar 

  34. A.F. Gosling, R. Thalappillil, J. Ortoleva, P. Datta, F.C. Cobey, Automated spectral Doppler profile tracing. J. Cardiothorac. Vasc. Anesth. 34(1), 72–76 (2020)

    Google Scholar 

  35. A.M. Bur, A. Holcomb, S. Goodwin, J. Woodroof, O. Karadaghy, Y. Shnayder, et al., Machine learning to predict occult nodal metastasis in early oral squamous cell carcinoma. Oral Oncol. 92, 20–25 (2019)

    Google Scholar 

  36. A. Maubert, L. Birtwisle, J.L. Bernard, E. Benizri, J.M. Bereder, Can machine learning predict resecability of a peritoneal carcinomatosis? Surg. Oncol. 29, 120–125 (2019)

    Google Scholar 

  37. R.F. Thompson, G. Valdes, C.D. Fuller, C.M. Carpenter, O. Morin, S. Aneja, et al., Artificial intelligence in radiation oncology: A specialty-wide disruptive transformation? Radiother. Oncol. 129(3), 421–426 (2018). https://doi.org/10.1016/j.radonc.2018.05.030

    Article  Google Scholar 

  38. M. Jakšič, M. Marinč, Relationship banking and information technology: The role of artificial intelligence and FinTech. Risk Manage. 21, 1–18 (2019)

    Google Scholar 

  39. M.E. Payne, J.W. Peltier, V.A. Barger, Mobile banking and AI-enabled mobile banking: The differential effects of technological and non-technological factors on digital natives’ perceptions and behavior. J. Res. Interact. Mark. 12(3), 328–346 (2018). https://doi.org/10.1108/JRIM-07-2018-0087

    Article  Google Scholar 

  40. R.S. Sexton, R.A. Johnson, M.A. Hignite, Predicting internet/ e-commerce use. Internet Res. 12(5), 402–410 (2002). https://doi.org/10.1108/10662240210447155

    Article  Google Scholar 

  41. X. Song, S. Yang, Z. Huang, T. Huang, The application of artificial intelligence in electronic commerce. J. Phys. Conf. Ser. 1302, 3 (2019)

    Google Scholar 

  42. R. Fildes, K. Nikolopoulos, S.F. Crone, A.A. Syntetos, Forecasting and operational research: a review. J. Oper. Res. Soc. 59(9), 1150–1172 (2008). https://doi.org/10.1057/palgrave.jors.2602597

    Article  MATH  Google Scholar 

  43. D. Bogataj, M. Bogataj, D. Hudoklin, Mitigating risks of perishable products in the cyber-physical systems based on the extended MRP model. Int. J. Prod. Econ. 193, 51–62 (2017)

    Google Scholar 

  44. J. Wan, J. Yang, Z. Wang, Q. Hua, Artificial Intelligence for cloud-assisted smart factory. IEEE Access. 6, 55419–55430 (2018)

    Google Scholar 

  45. M.H. Huang, R. Rust, Artificial Intelligence in service. J. Serv. Res. 21(2), 155–172 (2018)

    Google Scholar 

  46. N. Syam, A. Sharma, Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice. Ind. Mark Manage. 69, 135–146 (2018)

    Google Scholar 

  47. D.D. Wu, S.-H. Chen, D.L. Olson, Business intelligence in risk management: Some recent progresses. Inform. Sci. 256, 1–7 (2014)

    Google Scholar 

  48. H. Min, Artificial intelligence in supply chain management: Theory and applications. Int. J. Logist. Res. Appl. 13(1), 13–39 (2010)

    Google Scholar 

  49. P. Tambe, P. Cappelli, V. Yakubovich, Artificial intelligence in human resources management: Challenges and a path forward. Calif. Manage. Rev. 61(4), 15–42 (2019)

    Google Scholar 

  50. N.F. Ryman-Tubb, P. Krause, W. Garn, How Artificial Intelligence and machine learning research impacts payment card fraud detection: A survey and industry benchmark. Eng. Appl. Artif. Intel. 76, 130–157 (2018)

    Google Scholar 

  51. D. Grewal, A.L. Roggeveen, J. Nordfält, The future of retailing. J. Retail. 93(1), 1–6 (2017)

    Google Scholar 

  52. M. Laanpere, K. Pata, P. Normak, H. Põldoja, Pedagogy-driven design of digital learning ecosystems. Comput. Sci. Inf. Syst. 11(1), 419–442 (2014)

    Google Scholar 

  53. R. Luckin, Machine Learning and Human Intelligence: The Future of Education for the 21st Century (UCL IOE, London, 2018)

    Google Scholar 

  54. V. Mayer-Schönberger, K. Cukier, Learning with Big data: The Future of Education (Boston/New York, Eamon Dolan Book, 2014)

    Google Scholar 

  55. M. Montebello, AI Injected e-Learning: The Future of Online Education (Springer, Berlin, 2017)

    Google Scholar 

  56. L. Metcalf, D.A. Askay, L.B. Rosenberg, Keeping humans in the loop: Pooling knowledge through artificial swarm intelligence to improve business decision making. Calif. Manage. Rev. 61(4), 84–109 (2019)

    Google Scholar 

  57. T.J. Loftus, P.J. Tighe, A.C. Filiberto, P.A. Efron, S.C. Brakenridge, A.M. Mohr, et al., Artificial Intelligence and surgical decision-making. JAMA Surg. 155(2), 148–158 (2020)

    Google Scholar 

  58. P. Mascagni, A. Vardazaryan, D. Alapatt, T. Urade, T. Emre, C. Fiorillo, P . Pessaux, D. Mutter, J. Marescaux, G. Costamagna, B. Dallemagne, N. Padoy, Artificial intelligence for surgical safety: Automatic assessment of the critical view of safety in laparoscopic cholecystectomy using deep learning. Ann. Surg. (2020). https://doi.org/10.1097/SLA.0000000000004351

  59. F. Dal Mas, D. Piccolo, L. Edvinsson, M. Skrap, S. D’Auria, Strategy innovation, intellectual capital management and the future of healthcare. The case of Kiron by Nucleode, in Knowledge, People, and Digital Transformation: Approaches for a Sustainable Future, ed. by F. Matos, V. Vairinhos, I. Salavisa, L. Edvinsson, M. Massaro, (Springer, Cham, 2020), pp. 119–131

    Google Scholar 

  60. F. Dal Mas, D. Piccolo, D. Ruzza, Overcoming cognitive bias through intellectual capital management. The case of pediatric medicine, in Intellectual Capital in the Digital Economy, ed. by P. Ordonez de Pablos, L. Edvinsson, (Routledge, London, 2020), pp. 123–133

    Google Scholar 

  61. L. Cobianchi, F. Dal Mas, A. Peloso, L. Pugliese, M. Massaro, C. Bagnoli, et al., Planning the full recovery phase: An Antifragile perspective on surgery after COVID-19. Ann. Surg. 272(6), e296–e299 (2020)

    Google Scholar 

  62. M.H. Jarrahi, Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Bus. Horiz. 61(4), 577–586 (2018)

    Google Scholar 

  63. M. Hengstler, E. Enkel, S. Duelli, Applied artificial intelligence and trust—The case of autonomous vehicles and medical assistance devices. Technol Forecast Soc Change 105, 105–120 (2016)

    Google Scholar 

  64. G.A. Montes, B. Goertzel, Distributed, decentralized, and democratized artificial intelligence. Technol Forecast Soc Change 141, 354–358 (2019)

    Google Scholar 

  65. N. Petit, Antitrust and artificial intelligence: A research agenda. J Eur Compet Law Pract. 8(6), 361–362 (2017)

    Google Scholar 

  66. A. Agrawal, J. Gans, A. Goldfarb, Economic policy for artificial intelligence. Innov. Policy Econ. 19, 139–159 (2018). https://doi.org/10.1086/699935

    Article  Google Scholar 

  67. A. Goldfarb, C. Tucker, Shifts in privacy concerns. Am. Econ. Rev. 102(3), 349–353 (2012)

    Google Scholar 

  68. A. Galasso, H. Luo, When does product liability risk chill innovation? Evidence from medical implants. Harvard Business School Working Paper, 2018

    Google Scholar 

  69. A. Shademan, R.S. Decker, J.D. Opfermann, S. Leonard, A. Krieger, P.C. Kim, Supervised autonomous robotic soft tissue surgery. Sci. Transl. Med. 8(337), 337–364 (2016)

    Google Scholar 

  70. E. Köse, N.N. Öztürk, S.R. Karahan, Artificial Intelligence in surgery. Eur Arch Med Res. 34(Suppl. 1), S4–S6 (2018)

    Google Scholar 

  71. G. Aruni, G. Amit, P. Dasgupta, New surgical robots on the horizon and the potential role of artificial intelligence. Investig. Clin. Urol. 59(4), 221–222 (2018)

    Google Scholar 

  72. A. Atabekov, O. Yastrebov, Legal status of artificial intelligence across countries: legislation on the move. Eur. Res. Stud. J. 21(4), 773–782 (2018)

    Google Scholar 

  73. H.J. Wilson, P.R. Daugherty, N. Morini-Bianzino, The jobs that artificial intelligence will create. MIT Sloan Manag. Rev. 58(4), 14–16 (2017)

    Google Scholar 

  74. Y.K. Dwivedi, L. Hughes, E. Ismagilova, G. Aarts, C. Coombs, T. Crick, et al., Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. Int. J. Inf. Manage. 2019, 101994 (2019)

    Google Scholar 

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Authors and Affiliations

  1. Instituto Universitário de Lisboa, Business Research Unit, Lisbon, Portugal

    Maria José Sousa

  2. Department of Management, Lincoln International Business School, University of Lincoln, Lincoln, UK

    Francesca Dal Mas

  3. Gabinete de Estratégia e Estudos do Ministério da Economia e Transição Digital, Lisbon, Portugal

    Gabriel Osório de Barros & Nuno Tavares

Authors
  1. Maria José Sousa
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  2. Francesca Dal Mas
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  3. Gabriel Osório de Barros
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  4. Nuno Tavares
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Correspondence to Francesca Dal Mas .

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Editors and Affiliations

  1. Bambala Institutional Area Pratap Nagar, Sanganer, Jaipuria Institute of Management, Jaipur, Rajasthan, India

    P. Mary Jeyanthi

  2. School of CSE, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India

    Tanupriya Choudhury

  3. Department of Management, Crandall University, Moncton, Canada

    Dieu Hack-Polay

  4. Department of informatics, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India

    T P Singh

  5. Computer Science and Engineering Department, Independent University, Dhaka, Bangladesh

    Sheikh Abujar

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Sousa, M.J., Dal Mas, F., Osório de Barros, G., Tavares, N. (2022). Artificial Intelligence: Technologies, Applications, and Policy Perspectives. Insights from Portugal. In: Jeyanthi, P.M., Choudhury, T., Hack-Polay, D., Singh, T.P., Abujar, S. (eds) Decision Intelligence Analytics and the Implementation of Strategic Business Management. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-82763-2_6

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