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Production and Manufacturing

Artificial intelligence an essential factor for the benefit of companies: systematic review of the literature

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Article: 2380344 | Received 14 Nov 2023, Accepted 20 Jun 2024, Published online: 21 Jul 2024

References

  • Abood, A., Nasser, A., & Al, H. (2022). Predictive maintenance of electromechanical systems using deep learning algorithms: Review. Ingénierie des systèmes d information, 27(6), 1009–1017. https://doi.org/10.18280/isi.270618
  • Abu, M. (2023). Smartening up ports digitalization with artificial intelligence (AI): A study of artificial intelligence business drivers of smart port digitalization. Management and Economics Review, 8(1), 78–97. https://doi.org/10.24818/mer/2023.02-06
  • Adam, S., Qiu, M., & Gai, K. (2016). Understanding taxonomy of cyber risks for cybersecurity insurance of financial industry in cloud computing. IEEE Xplore, 295–300. https://doi.org/10.1109/CSCloud.2016.46
  • Akhtar, M., & Feng, T. (2021). An overview of the applications of artificial intelligence in cybersecurity. EAI Endorsed Transactions on Creative Technologies, 8(29), 172218. https://doi.org/10.4108/eai.23-11-2021.172218
  • Alloulbi, A., Öz, T., & Alzubi, A. (2022). The use of artificial intelligence for smart decision-making in smart cities: A moderated mediated model of technology anxiety and internal threats of IoT. Mathematical Problems in Engineering, 2022, 1–12. https://doi.org/10.1155/2022/6707431
  • Almobarek, M., Mendibil, K., & Alrashdan, A. (2023). Predictive maintenance 4.0 for children water system at commercial buildings: A methodological framework. Buildings, 13(2), 497. https://doi.org/10.3390/buildings13020497
  • Alneyadi, S., Abulibdeh, E., & Wardat, Y. (2023). The impact of digital environment vs. traditional method on literacy skills; reading and writing of emirati fourth graders. Sustainability, 15(4), 3418. https://doi.org/10.3390/su15043418
  • Arjoune, Y., & Faruque, S. (2020). Artificial intelligence for 5G wireless systems: opportunities, challenges, and future research direction. IEEE Xplore, 1023–1028. https://doi.org/10.1109/CCWC47524.2020.9031117
  • Ayadi, R., Abd El-Aziz, R. M., Taloba, A. I., Aljuaid, H., Hamed, N. O., & Khder, M. A. (2022). Deep learning-based soft sensors for improving the flexibility for automation of industry. Wireless Communications and Mobile Computing, 2022, 1–10. https://doi.org/10.1155/2022/5450473
  • Aydin, Y., Sirintuna, D., & Basdogan, C. (2020). Towards collaborative drilling with a cobot using admittance controller. Transactions of the Institute of Measurement and Control, 43(8), 1760–1773. https://doi.org/10.1177/0142331220934643
  • Baker, J. (2020). Management perspective of ethics in artificial intelligence. AI and Ethics, 12, 173–181. https://doi.org/10.1007/s43681-020-00022-3
  • Banitaan, S., Al, G., Almatarneh, S., & Alquran, H. (2023). A review on artificial intelligence in the context of industry 4.0. International Journal of Advanced Computer Science and Applications, 14, 1–8. https://doi.org/10.1016/j.eswa.2022.119456
  • Banu, F., Neelakandan, S., Geetha, B., Selvalakshmi, V., Umadevi, A., & Ofori, E. (2022). Artificial intelligence based customer churn prediction model for business markets. Computational Intelligence and Neuroscience, 2022, 1703696. https://doi.org/10.1155/2022/1703696
  • Bi, M., Luo, C., Miao, Z., Zhang, B., Zhang, W., & Wang, L. (2021). Safety assurance mechanism of collaborative robotic systems in manufacturing. Robotics and Computer-Integrated Manufacturing, 67, 102022. https://doi.org/10.1016/j.rcim.2020.102022
  • Borboni, A., Reddy, K. V. V., Elamvazuthi, I., Al-Quraishi, M. S., Natarajan, E., & Azhar Ali, S. S. (2023). The expanding role of artificial intelligence in collaborative robots for industrial applications: A systematic review of recent works. Machines, 11(1), 111. https://doi.org/10.3390/machines11010111
  • Calitz, A., Poisat, P., & Cullen, M. (2017). The future African workplace: The use of collaborative robots in manufacturing. SA Journal of Human Resource Management, 1(2), 1–11. https://doi.org/10.4102/sajhrm.v15i0.901
  • Carvalho, T., Soares, F., Vita, R., Francisco, R., Basto, J., & Alcalá, S. (2019). A systematic literature review of machine learning methods applied to predictive maintenance. Computers & Industrial Engineering, 137, 106024. https://doi.org/10.1016/j.cie.2019.106024
  • Chen, S. (2021). Application and thinking of artificial intelligence in electrical automation. Wireless Communications and Mobile Computing, 2022, 1–6. https://doi.org/10.1155/2022/2609156
  • Cheng, X., Chaw, J. K., Goh, K. M., Ting, T. T., Sahrani, S., Ahmad, M. O. H N., Abdul Kadir, R., & Ang, M. C. (2022). Systematic literature review on visual analytics of predictive maintenance in the manufacturing industry. Sensors (Basel, Switzerland), 22(17), 6321. https://doi.org/10.3390/s22176321
  • Cheng, Y., Sun, L., & Tomizuka, M. (2021). Human-aware robot task planning based on a hierarchical task model. IEEE Robotics and Automation Letters, 6(2), 1136–1143. https://doi.org/10.1109/LRA.2021.3056370
  • Chromjakova, F., Trentesaux, D., & Adu, Adu, M. (2021). Human and cobot cooperation ethics: The process management concept of the production workplace. Journal of Competitiveness, 13(3), 21–38. https://doi.org/10.7441/joc.2021.03.02
  • Clim, A., Toma, A., Daniel, R., & Constantinescu, R. (2022). The need for cybersecurity in industrial revolution and smart cities. Sensors, 23(1), 120. https://doi.org/10.3390/s23010120
  • Costa, D. A. D S., Mamede, H. S., & Mira da Silva, M. (2022). Robotic process automation (RPA) adoption: A systematic literature review. Engineering Management in Production and Services, 14(2), 1–12. https://doi.org/10.2478/emj-2022-0012
  • Das, R., & Sandhane, R. (2021). Artificial intelligence in cyber security. Journal of Physics: Conference Series, 1964(4), 042072. https://doi.org/10.1088/1742-6596/1964/4/042072
  • Dawson, M. (2021). Cybersecurity impacts for artificial intelligence use within industry 4.0. Scientific Bulletin, 26(1), 24–31. https://doi.org/10.2478/bsaft-2021-0003
  • Deepika, B., Chong, W., & Gert, G. (2022). User fears and challenges in the adoption of networks automation. International Journal of Design, Analysis & Tools for Integrated Circuits & Systems, 11, 70–75. https://research.ebsco.com/c/ylm4lv/details/ufeozbdwmv
  • Dehler, J., Marvin, O., & Dogan, K. (2021). The legitimacy of wind power in Germany. Karlsruhe Institute of Technology, 54, 1–38. https://doi.org/10.5445/IR/1000128597
  • Dirican, C. (2015). The impacts of robotics, artificial intelligence on business and economics. Procedia - Social and Behavioral Sciences, 195, 564–573. https://doi.org/10.1016/j.sbspro.2015.06.134
  • Drakaki, M., Karnavas, Y., Tziafettas, I., Linardos, V., & Tziomas, P. (2022). Machine learning and deep learning-based methods toward industry 4.0 predictive maintenance in induction motors: State of the art survey. Journal of Industrial Engineering and Management, 15, 31–57. https://doi.org/10.1109/TAI.2021.3054609
  • El, S., Wang, Y., Hu, Y., & Li, W. (2022). An improved approach of task-parameterized learning from demonstration for cobots in dynamic manufacturig. Journal of Intelligent Manufacturing, 33(5), 1503–1519. https://doi.org/10.1007/s10845-021-01743-w
  • Eslami, M., Kumaran, S., Sandvig, C., & Karahalios, K. (2018 Communicating algorithmic process in online behavioral advertising [Paper presentation]. CH. Proce. Ch. Confe. Hum. Fact. Compu. Syst (pp. 1–13). https://doi.org/10.1145/3173574.3174006
  • Farooqi, N., & Khozium, M. (2022). Implementation of artificial intelligence-based analyzer using multi-agent system approach. Intelligent Automation & Soft Computing, 31(1), 297–309. https://doi.org/10.32604/iasc.2022.019060
  • Fonseca, T., Chaves, P., Ferreira, L., Gouveia, N., Costa, D., Oliveira, A., & Landeck, J. (2023). Dataset for identifying maintenance needs of home appliances using artificial intelligence. Data in Brief, 48, 109068. https://doi.org/10.1016/j.dib.2023.109068
  • Guan, H., Dong, L., & Zhao, A. (2022). Ethical risk factors and mechanisms in artificial intelligence decision making. Behavioral Sciences (Basel, Switzerland), 12(9), 343. https://doi.org/10.3390/bs12090343
  • Horák, J., & Turková, M. (2023). Using artificial intelligence as business opportunities on the market: An overview. SHS Web of Conferences, 160, 01012. https://doi.org/10.1051/shsconf/202316001012
  • Ibrahim, Y., Al, W., Hamad, A., Nouri, A., & Meraf, Z. (2022). Perception of the impact of artificial intelligence in the decision-making processes of public healthcare professionals. Journal of Environmental and Public Health, 2022, 8028275. https://doi.org/10.1155/2022/8028275
  • Ivanovich, S. (2021). Social problems of decision making by artificial intelligence in a digital society. Social Journal, 27, 90–108. https://doi.org/10.19181/socjour.2021.27.2.8088
  • Jan, Z., Ahamed, F., Mayer, W., Patel, N., Grossman, G., Stumptner, M., & Kuusk, A. (2023). Artificial intelligence for industry 4.0: Systematic review of applications, challenges, and opportunities. Expert Systems with Applications, 216, 119456. https://doi.org/10.1016/j.eswa.2022.119456
  • Javaid, M., Haleem, A., Pratap, R., & Suman, R. (2022). Artificial intelligence applications for industry 4.0: A literature-based study. Journal of Industrial Integration and Management, 07(01), 83–111. https://doi.org/10.1142/S2424862221300040
  • Javaid, M., Haleem, A., Singh, R. P., Rab, S., & Suman, R. (2022). Significant applications of cobots in the field of manufacturing. Cognitive Robotics, 2, 222–233. https://doi.org/10.1016/j.cogr.2022.10.001
  • Kang, X., & Zeng, Y. (2022). Entrepreneurial bricolage based on big data and artificial intelligence decision making. Wireless Communications and Mobile Computing, 2022, 1–7. https://doi.org/10.1155/2022/7821069
  • Kóczi, D., & Sárosi, J. (2022). The safety of collaborative robotics-A review. ANNALS of Faculty Engineering Hunedoara – International Journal of Engineering, 2, 1–5. https://www.researchgate.net/publication/363298471_THE_SAFETY_OF_COLLABORATIVE_ROBOTICS_-A_REVIEW
  • Kravets, A. (2020). Robotics: Industry 4.0 Issues & new intelligent control paradigms. In Stud. Syst. Dec. Contr (pp. 1–237). https://doi.org/10.1007/978-3-030-37841-7
  • Kumar, S., Kumudham, R., Kumar, R., Dhamodharan, M., & Vetrivel, S. (2022 Smart home automation using raspberry PI 4 [Paper presentation]. Aip. Confe. Proce. 2463 (pp. 151–158). https://doi.org/10.1063/5.0080751
  • Lefranc, G., Lopez, I., Osorio, R., & Peña, M. (2022). Impact of cobots on automation. Procedia Computer Science, 214, 71–78. https://doi.org/10.1016/j.procs.2022.11.150
  • Li, H. (2022). Research on the significance of big data and artificial intelligence technology to enterprise business management. Mobile Information Systems, 2022, 1–10. https://doi.org/10.1155/2022/7639965
  • Li, Y. (2022). Application analysis of artificial intelligent neural network based on intelligent diagnosis. Procedia Computer Science, 208, 31–35. https://doi.org/10.1016/j.procs.2022.10.006
  • Li, Y., Sena, A., Wang, Z., Xing, X., Babič, J., Asseldonk, E., & Burdet, E. (2022). A review on interaction control for contact robots through intent detection. Progress in Biomedical Engineering 4, 1–22. https://doi.org/10.1088/2516-1091/ac8193
  • Liu, L., & Hu, Z. (2022). Big data analysis technology for artificial intelligence decision-making platform construction and application. Mobile Information Systems, 2022, 1–12. https://doi.org/10.1155/2022/3052469
  • Lv, Q., Zhang, R., Liu, T., Zheng, P., Jiang, Y., Li, J., Bao, J., & Xiao, L. (2022). A strategy transfer approach for intelligent human-robot collaborative assembly. Computers & Industrial Engineering, 168, 108047. https://doi.org/10.1016/j.cie.2022.108047
  • Market and markets. (2023). Collaborative robot market. Mark. Rese. Repo https://www.marketsandmarkets.com/Market-Reports/collaborative-robot-market-194541294.html?gclid=EAIaIQobChMIwY_Mg6_BgAMVWimtBh2YPQVbEAAYASAAEgKBE_D_BwE
  • Masoodifar, M., Kahraman, I., & Tümbek, A. (2023). Artificial intelligence in global business and its communication. Journal of International Trade, Logistics and Law, 208, 278–284. http://jital.org/index.php/jital/article/view/371
  • Meduri, Y., & Yadav, P. (2021). Automation invading human resources: digital transformation and impact of automation in the space of HR. Delhi Business Review, 22(1), 62–69. https://doi.org/10.51768/dbr.v22i1.221202105
  • Mesarosova, J., Martinovicova, K., Fidlerova, H., Hrablik Chovanova, H., Babcanova, D., & Samakova, J. (2022). Improving the level of predictive maintenance maturity matrix in industrial enterprise. Acta Logistica, 9(2), 183–193. https://doi.org/10.22306/al.v9i2.292
  • Mirbabaie, M., Stieglitz, S., & Frick, N. (2021). Artificial intelligence in disease diagnostics: A critical review and classification in current state of research guiding future direction. Health and Technology, 11(4), 693–731. https://doi.org/10.1007/s12553-021-00555-5
  • Mulders, M. (2017). Predictive maintenance 4.0-predict the unpredictable. Pdm. Main. Inno (pp. 1–32). https://www.pwc.be/en/documents/20171016-predictive-maintenance-4-0.pdf
  • Nicolau, A. (2021). Artificial intelligence in business: Present and perspective. Romanian Journal of Economics, Institute of National Economy, 1–10. https://doi.org/10.1051/shsconf/202316001012
  • Niu, S., Liu, Y., Wang, J., & Song, H. (2020). A decade survey of transfer learning (2010-2020). IEEE Transactions on Artificial Intelligence, 1(2), 151–166. https://doi.org/10.1109/TAI.2021.3054609
  • Oleksiewicz, I. (2022). Artificial intelligence versus human-a threat or a necessity of evolution. ICI Journals Master List, 3, 12–29. https://doi.org/10.31338/1641-2478pe.3.22.4
  • Pech, M., Vrchota, J., & Bednář, J. (2021). Predictive maintenance and intelligent sensors in smart factory: review. Sensors (Basel, Switzerland), 21(4), 1470. https://doi.org/10.3390/s21041470
  • Pedretti, A., Santini, M., Scolimoski, J., Brito, M., Toshioka, F., Rocha, E., Pauli, N., Takashi, M. ; D., Costa, C., Guerra, F., Mulinari, B., Grando, F., Costa, J., Almeida, C., Lambert, G., & Pires, M. (2021). Robotic process automation extended with artificial intelligence techniques in power distribution utilities. Brazilian Archives of Biology and Technology, 64(spe), 1–12. https://doi.org/10.1590/1678-4324-75years-2021210217
  • Peng, Y., & Krutasaen, W. (2022). National costume art design optimization under the background of artificial intelligence decision making and internet of things. Mathematical Problems in Engineering, 2022, 1–6. https://doi.org/10.1155/2022/4803617
  • Pinto, A., Sousa, S., Simões, A., & Santos, J. (2022). A trust scale for human-robot interaction: Translation, adaptation, and validation of a human computer trust scale. Human Behavior and Emerging Technologies, 2022, 1–12. https://doi.org/10.1155/2022/6437441
  • Pizoń, J., Cioch, M., Kański, L., & Sánchez, E. (2022). Cobots implementation in the era of industry 5.0 using moder business and management solutions. Advances in Science and Technology Research Journal, 16(6), 166–178. https://doi.org/10.12913/22998624/156222
  • Pizoń, J., Gola, A., & Świć, A. (2022). The role and meaning of the digital twin technology in the process of implementing intelligent collaborative robots. International Scientific-Technical Conference Manufacturing, 39–49. https://doi.org/10.1007/978-3-031-00805-4_4
  • Praveenraj, D., Victor, M., Vennila, C., Alawadi, A., Diyora, P., Vasudevan, N., & Avudaiappan, T. (2023). Exploring explainable artificial intelligence for transparent decision making. E3S Web of Conferences, 399, 04030. https://doi.org/10.1051/e3sconf/202339904030
  • Qilin, W., & Yue, Z. (2022). Automation design and organization innovation of manufacturing enterprises based on internet of things. Scientific Programmes, 2022, 1–12. https://doi.org/10.1155/2022/8729731
  • Quirumbay, D., Castillo, C., & Coronel, I. (2022). Una revisión del aprendizaje profundo aplicado a la ciberseguridad. Revista Científica y Tecnológica UPSE, 9, 1–9. https://doi.org/10.26423/rctu.v9i1.671
  • Rajagopal, N. K., Qureshi, N. I., Durga, S., Ramirez Asis, E. H., Huerta Soto, R. M., Gupta, S. K., & Deepak, S. (2022). Future of business culture: An artificial intelligence-driven digital framework for organization decision-making process. Complexity, 2022, 1–14. https://doi.org/10.1155/2022/7796507
  • Refaat Hassan, M., Alkhalaf, S., Mohamed Hemeida, A., Ahmed, M., & Mahmoud, E. (2023). Artificial intelligence applications for estimating flow network reliability. Ain Shams Engineering Journal, 14(8), 102055. https://doi.org/10.1016/j.asej.2022.102055
  • Sabin, B., Turcan, D., Răpan, M., & Banta, C. (2023). Fusion between artificial intelligence and automotive sales market. Annals - Economy Series, 66–82. https://www.utgjiu.ro/revista/ec/pdf/2023-01/25_Babeanu.pdf
  • Saeed, S., Suayyid, S., Ghamdi, M., Muhaisen, H., & Almuhaideb, A. (2023). A systematic literature review on cyber threat intelligence for organizational cybersecurity resilience. Sensors (Basel, Switzerland), 23(16), 1–27. https://doi.org/10.3390/s23167273
  • Salunkhe, O., Fager, P., & Berglund, Å. (2020 Framework for identifying gripper requirements for collaborative robot applications in manufacturing [Paper presentation]. IFIP. Inter. Conf. on Adv. In Prod. Manag. Syst. https://doi.org/10.1007/978-3-030-57993-7_74
  • Salunkhe, O., Fager, P., & Fast, Å. (2020). Framework for identify gripper requirements for collaborative robot applications in manufacturing. Advances in Production Management Systems, 591, 655–662. https://link.springer.com/chapter/10.1007/978-3-030-57993-7_74
  • Saman, M., Flammini, F., Santini, S., & Caporuscio, M. (2023). A systematic literature review on transfer learning for predictive in industry 4.0. IEEE Xplore, 11, 12887–12910. https://doi.org/10.1109/ACCESS.2023.3239784
  • Siderska, J. (2020). Robotic process automation-A driver of digital transformation. Engineering Management in Production and Services, 12, 21–31. https://doi.org/10.2478/emj-2020-0009
  • Singla, D., Cimen, F., & Aluganti, C. (2022). Novel artificial intelligent transformer U-NET for better identification and management of prostate cancer. Molecular and Cellular Biochemistry, 478(7), 1439–1445. https://doi.org/10.1007/s11010-022-04600-3
  • Sourdin, T. (2018). Judge v robot? Artificial intelligence and judicial decision-making. University of New South Wales Law Journal, 41(4), 1–12. https://doi.org/10.53637/ZGUX2213
  • Steenwinckel, B., Paepe, D., Hautte, S., Heyvaert, P., Bentefrit, M., Moens, P., Dimou, A., Bossche, B., Turck, F., Hoecke, S., & Ongenae, F. (2021). FLAGS; A methodology for adaptive anomaly detection and root cause analysis on sensor data streams by fusing expert knowledge with machine learning. Future Generation Computer Systems, 116, 30–48. https://doi.org/10.1016/j.future.2020.10.015
  • Subramayam, N., & Patagundi, B. (2018). Automation and artificial intelligence-boon or bane: a humanistic perspective. Organization Management Quarterly, 4, 151–158. https://doi.org/10.29119/1899-6116.2018.44.10
  • Suescún, E., Pardo, C., Rojas, S., & Velásquez, A. (2021). DevOps in industry 4.0: A systematic mapping. Redacción de Informes, 1–17. https://doi.org/10.19053/01211129.v30.n57.2021.13314
  • Taesi, C., Aggogeri, F., & Pellegrini, N. (2023). Cobot applications-recent advances and challenges. Robotics, 12, 1(3), 79. https://doi.org/10.3390/robotics12030079
  • Tao, F., Akhtar, M., & Jiayuan, Z. (2021). The future of artificial intelligence in cybersecurity: A comprehensive survey. EAI Endorsed Transactions on Creative Technologies, 8(28), 170285. https://doi.org/10.4108/eai.7-7-2021.170285
  • Tobarra, L., Utrilla, A., Robles, A., Pastor, R., & Hernández, R. (2021). A cloud game-based educative platform architecture: The cyberscratch project. Applied Sciences, 11(2), 807. https://doi.org/10.3390/app11020807
  • Uskenbayeva, R., Kalpeyeva, Z., Satybaldiyeva, R., Moldagulova, A., & Kassymova, A. (2019). Applying of RPA in administrative processes of public administration. IEEE Xplore, 22, 9–12. https://doi.org/10.1109/CBI.2019.10089
  • Utkina, M., Bondarenko, O., Chernadchuk, T., & Chernadchuk, O. (2023). Intellectual property rights on objects created by artificial intelligence. Law, State and Telecommunications Review, 15(1), 85–105. https://doi.org/10.26512/lstr.v15i1.41729
  • Vergara, O., Nandayapa, M., Sossa, J., Cossio, E., & Rubin, G. (2021). Artificial intelligent for industry 4.0 in Iberoamérica. Computación y Sistemas, 25, 1–13. https://doi.org/10.13053/cys-25-4-4056
  • Vermesan, U., John, R., De Luca, C., & Coppola, M. (2021). Artificial intelligence for digitising industry applications. Riv. Pub. Ser. Comu.
  • Villa, V., Bruno, G., Aliev, K., Piantanida, P., Corneli, A., & Antonelli, D. (2022). Machine learning framework for the sustainable maintenance of building facilities. Sustainability, 14(2), 681. https://doi.org/10.3390/su14020681
  • Waqas, M., & Amin, M. (2022). The use of artificial intelligence in the context of business to consumers firms in Pakistan. KASBIT Business Journal, 66–82. https://essentials.ebsco.com/search/eds/details/the-use-of-artificial-intelligence-in-the-context-of-business-to-consumers-firms-in-pakistan?query=business%20and%20artificial%20intelligence&requestCount=0&db=owf&an=158237134
  • Weckenborg, C., & Spengler, T. (2019). Assembly line balancing with collaborative robots under consideration of ergonomics: a cost-oriented approach. IFAC-PapersOnLine, 52(13), 1860–1865. https://doi.org/10.1016/j.ifacol.2019.11.473
  • Xia, L. (2021). The indoor space layout of university laboratories based on wireless communication and artificial intelligence decision-making. Wireless Communications and Mobile Computing, 2022, 1–7. https://doi.org/10.1155/2022/5121762
  • Yang, J., Chen, Y., Huang, W., & Li, Y. (2017). Survey on artificial intelligence for additive manufacturing. IEEE Xplore, 1–6. https://doi.org/10.23919/IConAC.2017.8082053
  • Zbigniew, P., Klimasara, W., Pachuta, M., & Słowikowski, M. (2019). Some new robotization problems related to the introduction of collaborative robots into industrial practice. Journal of Automation, Mobile Robotics and Intelligent Systems, 4, 91–97. https://doi.org/10.14313/JAMRIS/4-2019/42
  • Zhang, J., Gisca, O., Dehkordi, R., & Ahokangas, P. (2022). Ecosystems legitimacy challenges in the platform, data, and artificial intelligence business model. Journal of Business Models, 10(1), 42–49. https://doi.org/10.54337/jbm.v10i1.6794
  • Zhang, X., Afari, M., Zhang, Y., & Xing, X. (2024). The impact of artificial intelligence on organizational justice and project performance: A systematic literature and science mapping review. Buildings, 14, 28. https://doi.org/10.3390/s22176321
  • Zonta, T., Da Costa, C., Da Rosa, R., De Lima, M., Da Trindade, E., & Pyng, G. (2020). Predictive maintenance in the industry 4.0: A systematic literature review. Computers & Industrial Engineering, 150, 106889. https://doi.org/10.1016/j.cie.2020.106889