The impact of artificial intelligence (AI) on employees’ skills and well-being in global labor markets: A systematic review
DOI:
https://doi.org/10.24136/oc.2023.022Keywords:
artificial intelligence (AI), employees, skills, digital skills, upskilling, reskilling, well-beingAbstract
Research background: This article discusses how artificial intelligence (AI) is affecting workers' personal and professional lives, because of many technological disruptions driven by the recent pandemic that are redefining global labor markets.
Purpose of the article: The objective of this paper is to develop a systematic review of the relevant literature to identify the effects of technological change, especially the adoption of AI in organizations, on employees’ skills (professional dimension) and well-being (personal dimension).
Methods: To implement the research scope, the authors relied on Khan's five-step methodology, which included a PRISMA flowchart with embedded keywords for selecting the appropriate quantitative data for the study. Firstly, 639 scientific papers published between March 2020 to March 2023 (the end of the COVID-19 pandemic according to the WHO) from Scopus and Web of Science (WoS) databases were selected. After applying the relevant procedures and techniques, 103 articles were retained, which focused on the professional dimension, while 35 papers were focused on the personal component.
Findings & value added: Evidence has been presented highlighting the difficulties associated with the ongoing requirement for upskilling or reskilling as an adaptive reaction to technological changes. The efforts to counterbalance the skill mismatch impacted employees' well-being in the challenging pandemic times. Although the emphasis on digital skills is widely accepted, our investigation shows that the topic is still not properly developed. The paper's most significant contributions are found in a thorough analysis of how AI affects workers' skills and well-being, highlighting the most representative aspects researched by academic literature due to the recent paradigm changes generated by the COVID-19 pandemic and continuous technological disruptions.
Downloads
References
Abdullah, K. H., & Sofyan, D. (2023). Machine learning in safety and health research: A scientometric analysis. International Journal of Information Science & Management, 21(1), 17–35.
View in Google Scholar
Abina, A., Batkovič, T., Cestnik, B., Kikaj, A., Kovačič Lukman, R., Kurbus, M., & Zidanšek, A. (2022). Decision support concept for improvement of sustainability-related competences. Sustainability, 14(14), 8539.
DOI: https://doi.org/10.3390/su14148539
View in Google Scholar
Abuselidze, G., & Mamaladze, L. (2021). The impact of artificial intelligence on employment before and during pandemic: A comparative analysis. Journal of Physics: Conference Series, 1840, 012040.
DOI: https://doi.org/10.1088/1742-6596/1840/1/012040
View in Google Scholar
Akanksha, J., Arun, J. C., & Arup, V. (2021). Rebooting employees: Upskilling for artificial intelligence in multinational corporations. International Journal of Human Resource Management, 33(6), 1179–1208.
View in Google Scholar
Al-Jubari, I., Mosbah, A., & Salem, S. F. (2022). Employee well-being during COVID-19 pandemic: The role of adaptability, work-family conflict, and organizational response. Sage Open, 12(3), 1096142.
DOI: https://doi.org/10.1177/21582440221096142
View in Google Scholar
Allen, M. (2022). Trainer upskilling and reskilling models in business education. BW Academic Journal, 1(1), 127–131.
View in Google Scholar
Andronie, M., Lăzăroiu, G., Iatagan, M., Hurloiu, I., Ștefănescu, R., & Dijmărescu, A., (2023a). Big data management algorithms, deep learning-based object detection technologies, and geospatial simulation and sensor fusion tools on the Internet of Robotic Things. ISPRS International Journal of Geo-Information, 12, 35.
DOI: https://doi.org/10.3390/ijgi12020035
View in Google Scholar
Andronie, M., Lăzăroiu, G., Iatagan, M., Uță, C., Ștefănescu, R., & Cocoșatu, M. (2021). Artificial intelligence-based decision-making algorithms, Internet of Things sensing networks, and deep learning-assisted smart process management in cyber-physical production systems. Electronics, 10, 2497.
DOI: https://doi.org/10.3390/electronics10202497
View in Google Scholar
Andronie, M., Lăzăroiu, G., Karabolevski, O. L., Ștefănescu, R., Hurloiu, I., & Dijmărescu, A. (2023b). Remote big data management tools, sensing and computing technologies, and visual perception and environment mapping algorithms in the Internet of Robotic Things. Electronics, 12, 22.
DOI: https://doi.org/10.3390/electronics12010022
View in Google Scholar
Anshari, M., & Hamdan, M. (2022). Understanding knowledge management and upskilling in Fourth Industrial Revolution: Transformational shift and SECI model. VINE Journal of Information and Knowledge Management Systems, 52(3), 373–393.
DOI: https://doi.org/10.1108/VJIKMS-09-2021-0203
View in Google Scholar
Arpat, B., Namal, M. K., Kocanci, M., & Yumurtaci, A. (2021). An assessment of the social work program in Turkey in terms of labour market experience and professional skill attainment. Amfiteatru Economic, 23(57), 548–569.
DOI: https://doi.org/10.24818/EA/2021/57/548
View in Google Scholar
Asokan, D. R., Huq, F. A., Smith, C. M., & Stevenson, M. (2022). Socially responsible operations in the industry 4.0 era: Post-COVID-19 technology adoption and perspectives on future research. International Journal of Operations & Production Management, 42(13), 185–217.
DOI: https://doi.org/10.1108/IJOPM-01-2022-0069
View in Google Scholar
Asravor, R. K., & Sackey, F. G. (2023). Impact of technology on macro-level employment and the workforce: What are the implications for job creation and job destruction in Ghana? Social Indicators Research, 168, 207–225.
DOI: https://doi.org/10.1007/s11205-023-03109-6
View in Google Scholar
Babapour, C. M., Hultberg, A., & Bozic Y. N. (2022). Post-pandemic office work: Perceived challenges and opportunities for a sustainable work environment. Sustainability, 14, 294.
DOI: https://doi.org/10.3390/su14010294
View in Google Scholar
Bellmann, L., & Hübler, O. (2020). Working from home, job satisfaction and work–life balance – robust or heterogeneous links? International Journal of Manpower, 42(3), 424–441.
DOI: https://doi.org/10.1108/IJM-10-2019-0458
View in Google Scholar
Bilal, H., & Varallyai, L. (2019). Will artificial intelligence take over human resources: Recruitment and selection? Network Intelligence Studies, 13, 21–30.
View in Google Scholar
Bjursell, C., Bergmo-Prvulovic, I., & Hedegaard, J. (2021). Telework and lifelong learning. Frontiers in Sociology, 6, 642277.
DOI: https://doi.org/10.3389/fsoc.2021.642277
View in Google Scholar
Brun-Schammé, A., & Rey, M. (2021). A new approach to skills mismatch. OECD Productivity Working Papers, 24.
View in Google Scholar
Bruun, E., & Duka, A. (2018). Artificial intelligence, jobs and the future of work: Racing with the machines. Basic Income Studies, 13(2), 20180018.
DOI: https://doi.org/10.1515/bis-2018-0018
View in Google Scholar
Carlisle, S., Ivanov, S., & Dijkmans, C. (2023). The digital skills divide: Evidence from the European tourism industry. Journal of Tourism Futures, 9(2), 240–266.
DOI: https://doi.org/10.1108/JTF-07-2020-0114
View in Google Scholar
Chatterjee, S., Chaudhuri, R., Vrontis, D., & Jabeen, F. (2022). Digital transformation of organization using AI-CRM: From micro foundational perspective with leadership support. Journal of Business Research, 153(C), 46–58.
DOI: https://doi.org/10.1016/j.jbusres.2022.08.019
View in Google Scholar
Chinn, D., Hieronimus, S., Kirchherr, J., & Klier, J. (2020). The future is now: Closing the skills gap in Europe’s public sector. McKinsey & Company. Retrieved from: https://www.mckinsey.com/industries/public-sector/our-insights/the-futu re-is-now-closing-the-skills-gap-in-europes-public-sector (2.05.2023).
View in Google Scholar
Chuang, S. (2022). Indispensable skills for human employees in the age of robots and AI. European Journal of Training and Development. Advance online publication.
DOI: https://doi.org/10.1108/EJTD-06-2022-0062
View in Google Scholar
Colquitt, J. A., Hill, E. T., & De Cremer, D. (2023). Forever focused on fairness: 75 years of organizational justice in Personnel Psychology. Personnel Psychology, 76, 413–435.
DOI: https://doi.org/10.1111/peps.12556
View in Google Scholar
Daniels, K. (2000). Measures of five aspects of affective well-being at work. Human Relations, 53(2), 275–294.
DOI: https://doi.org/10.1177/a010564
View in Google Scholar
Davenport, T. H., & Mittal, N. (2023). How companies can prepare for the coming “AI-first” world. Strategy & Leadership, 51(1), 26–30.
DOI: https://doi.org/10.1108/SL-11-2022-0107
View in Google Scholar
Davidescu, A. A., Apostu, S.-A., Paul, A., & Casuneanu, I. (2020). Work flexibility, job satisfaction, and job performance among Romanian employees—implications for sustainable human resource management. Sustainability, 12, 6086.
DOI: https://doi.org/10.3390/su12156086
View in Google Scholar
De Notaris, D. (2019). Reskilling higher education professionals. In M. Calise, C. Delgado Kloos, J. Reich, J. Ruiperez-Valiente & M. Wirsing (Eds.). Digital education: At the MOOC crossroads where the interests of academia and business converge. (pp. 146–155). Springer.
DOI: https://doi.org/10.1007/978-3-030-19875-6_17
View in Google Scholar
Dicuonzo, G., Donofrio, F., Fusco, A., & Shini, M. (2023). Healthcare system: Moving forward with artificial intelligence. Technovation, 120, 102510.
DOI: https://doi.org/10.1016/j.technovation.2022.102510
View in Google Scholar
Doellgast, V., Wagner, I., & O’Brady, S. (2023). Negotiating limits on algorithmic management in digitalised services: cases from Germany and Norway. Transfer: European Review of Labour and Research, 29(1), 105–120.
DOI: https://doi.org/10.1177/10242589221143044
View in Google Scholar
Doran, N. M., Bădîrcea, R. M., & Manta, A. G. (2022). Digitization and financial performance of banking sectors facing COVID-19 challenges in Central and Eastern European Countries. Electronics, 11, 3483.
DOI: https://doi.org/10.3390/electronics11213483
View in Google Scholar
Dosi, G., Piva, M., Virgillito, M. E., & Vivarelli, M. (2021). Embodied and disembodied technological change: The sectoral patterns of job-creation and job-destruction. Research Policy, 50(4), 104199.
DOI: https://doi.org/10.1016/j.respol.2021.104199
View in Google Scholar
Eberhard, B., Podio, M., Alonso, A. P., Radovica, E., Avotina, L., Peiseniece, L., Sendon, M. C., Lozano, A. G., & Solé-Pla, J. (2017). Smart work: The transformation of the labour market due to the fourth industrial revolution (I4.0). International Journal of Business and Economic Sciences Applied Research, 10(3), 47–66.
View in Google Scholar
Ercantan, O., & Eyupoglu, S. (2022). How do green human resource management practices encourage employees to engage in green behavior? Perceptions of university students as prospective employees. Sustainability, 14, 1718.
DOI: https://doi.org/10.3390/su14031718
View in Google Scholar
Escudero-Castillo, I., Mato-Díaz, F. J., & Rodríguez-Alvarez, A. (2023). Psychological well-being during the COVID-19 lockdown: Labour market and gender implications. Applied Research Quality Life, 18, 71–91.
DOI: https://doi.org/10.1007/s11482-022-10113-4
View in Google Scholar
Eurofound (2020). Labour market change: Trends and policy approaches towards flexibilization. Challenges and prospects in the EU series. Luxembourg: Publications Office of the European Union.
View in Google Scholar
Falahat, M., Cheah, P. K., Jayabalan, J., Lee, C. M. J., & Kai, S. B. (2023). Big data analytics capability ecosystem model for SMEs. Sustainability, 15, 360.
DOI: https://doi.org/10.3390/su15010360
View in Google Scholar
Foa, R., Gilbert, S., & Fabian, M. O. (2020). COVID-19 and subjective well-being: Separating the effects of lockdowns from the pandemic. SSRN.
DOI: https://doi.org/10.2139/ssrn.3674080
View in Google Scholar
Fredström, A., Parida, V., Wincent, J., Sjödin, D., Oghazi, P. J. T. F., & Change, S. (2022). What is the market value of artificial intelligence and machine learning? The role of innovativeness and collaboration for performance. Technological Forecasting and Social Change, 180, 121716.
DOI: https://doi.org/10.1016/j.techfore.2022.121716
View in Google Scholar
Graetz, G. (2020). Labor demand in the past, present, and future. IZA Discussion Paper, 13142.
DOI: https://doi.org/10.2139/ssrn.3579234
View in Google Scholar
Grenčíková, A., Kordoš, M., Bartek, J., & Berkovič, V. (2021). The impact of the Industry 4.0 concept on Slovak business sustainability within the issue of the pandemic outbreak. Sustainability, 13, 4975.
DOI: https://doi.org/10.3390/su13094975
View in Google Scholar
Habánik, J., Grenčíková, A., Šrámka, M., & Húževka, M. (2021). Changes in the organization of work under the influence of COVID-19 pandemic and Industry 4.0. Economics and Sociology, 14(4), 228–241. doi:10.14254/2071-789X.2021 /14- 4/13.
DOI: https://doi.org/10.14254/2071-789X.2021/14-4/13
View in Google Scholar
Hai, T. N., Van, Q. N., & Thi Tuyet, M. N. (2021). Digital transformation: Opportunities and challenges for leaders in the emerging countries in response to COVID-19 pandemic. Emerging Science Journal, 5(1), 21–36.
DOI: https://doi.org/10.28991/esj-2021-SPER-03
View in Google Scholar
Hemin, Q. (2018). Will artificial intelligence brighten or threaten the future. MNSES9100 - Science, ethics and society. Retrieved from https://www.research gate.net/publication/323535179_Will_Artificial_Intelligence_Brighten_or_Threat en_the_Future (17.04.2023).
View in Google Scholar
Henderikx, M., & Stoffers, J. (2022). An exploratory literature study into digital transformation and leadership: Toward future-proof middle managers. Sustainability, 14(2), 687.
DOI: https://doi.org/10.3390/su14020687
View in Google Scholar
Henkel, A. P., Bromuri, S., Iren, D., & Urovi, V. (2020). Half human, half machine – augmenting service employees with AI for interpersonal emotion regulation. Journal of Service Management, 31(2), 247–265.
DOI: https://doi.org/10.1108/JOSM-05-2019-0160
View in Google Scholar
Horobet, A., Popoviciu, A. S., Zlatea, E., & Alexe, R. (2021). The Eastern European automotive industry in a post-pandemic world: What drives performance? KnE Social Sciences, 5(9), 90–108.
DOI: https://doi.org/10.18502/kss.v5i9.9887
View in Google Scholar
Hussain, S., Singh, A. M., Mohanty, P., & Gavinolla, M. R. (2023). Next generation employability and career sustainability in the hospitality industry 5.0. Worldwide Hospitality and Tourism Themes, 15(3), 308–321.
DOI: https://doi.org/10.1108/WHATT-01-2023-0011
View in Google Scholar
ILO (2021). Skilling, upskilling and reskilling of employees, apprentices & interns during the COVID-19 pandemic. Findings from a global survey of enterprises. Geneva: International Labour Organization.
View in Google Scholar
ILO (2022). World employment and social outlook, trends 2022. International Labour Organization.
DOI: https://doi.org/10.1002/wow3.179
View in Google Scholar
Jaiswal, A. C., Arun, J., & Varma, A. (2022). Rebooting employees: Upskilling for artificial intelligence in multinational corporations. International Journal of Human Resource Management, 33(6), 1179–1208.
DOI: https://doi.org/10.1080/09585192.2021.1891114
View in Google Scholar
James, O., Han, C., & Tomasi, S. (2021). Using neural networks to predict wages based on worker skills. Studies in Business and Economics, 16(1), 95–108.
DOI: https://doi.org/10.2478/sbe-2021-0008
View in Google Scholar
Jamwal, A., Agrawal, R., & Sharma, M. (2022). Deep learning for manufacturing sustainability: Models, applications in Industry 4.0 and implications. International Journal of Information Management Data Insights, 2, 100107.
DOI: https://doi.org/10.1016/j.jjimei.2022.100107
View in Google Scholar
Jashari, X., Fetaji, B., Nussbaumer, A., & Gütl, C. (2021). Assessing digital skills and competencies for different groups and devising a conceptual model to support teaching and training. In M. Auer & D. May (Eds.). Cross reality and data science in engineering (pp. 982–995). Springer.
DOI: https://doi.org/10.1007/978-3-030-52575-0_82
View in Google Scholar
Jiang, F., Wang, L., Li, J.-X., & Liu, J. (2022). How smart technology affects the well-being and supportive learning performance of logistics employees? Frontiers in Psychology, 12, 768440.
DOI: https://doi.org/10.3389/fpsyg.2021.768440
View in Google Scholar
Joamets, K., & Chochia, A. (2020). Artificial intelligence and its impact on labour relations in Estonia. Slovak Journal of Political Sciences, 20(2), 255–277.
DOI: https://doi.org/10.34135/sjps.200204
View in Google Scholar
Kaltiainen, J., & Hakanen, J. J. (2023). Why increase in telework may have affected employee well-being during the COVID-19 pandemic? The role of work and non-work life domains. Current Psychology. Advance online publication.
DOI: https://doi.org/10.1007/s12144-023-04250-8
View in Google Scholar
Kanchibhotla, D., Saisudha, B., Ramrakhyani, S., & Mehta, D. H. (2021). Impact of a yogic breathing technique on the well-being of healthcare professionals during the COVID-19 pandemic. Global Advances in Health and Medicine, 10, 1–8.
DOI: https://doi.org/10.1177/2164956120982956
View in Google Scholar
Kar, S., Kar, A. K., & Gupta, M. P. (2022). Modeling drivers and barriers of artificial intelligence adoption: Insights from a strategic management perspective. International Journal of Intelligent Systems Accounting and Financial Management, 28(4), 217–238.
DOI: https://doi.org/10.1002/isaf.1503
View in Google Scholar
Kateryna, A., Oleksandr, R., Mariia, T., Iryna, S., Evgen, K., & Anastasiia, L. (2020). Digital literacy development trends in the professional environment. International Journal of Learning, Teaching and Educational Research, 19(7), 55–79.
DOI: https://doi.org/10.26803/ijlter.19.7.4
View in Google Scholar
Khan, M. A., Kamal, T., Illiyan, A., & Asif, M. (2023). School students’ perception and challenges towards online classes during COVID-19 pandemic in India: An econometric analysis. Sustainability, 13(9), 4786.
DOI: https://doi.org/10.3390/su13094786
View in Google Scholar
Khogali, H., & Mekid, S. (2022). The blended future of automation and AI: Examining some long-term societal impact features. SSRN.
DOI: https://doi.org/10.2139/ssrn.4239580
View in Google Scholar
Kolo, I., & Zuva, T. (2022). Trends in the adoption and acceptance of technology: Challenges and open issues. In R. Silhavy (Ed.). Software engineering perspectives in systems (pp. 726–736). Springer.
DOI: https://doi.org/10.1007/978-3-031-09070-7_60
View in Google Scholar
Korzynski, P., Kozminski, A. K., & Baczynska, A. (2023). Navigating leadership challenges with technology: Uncovering the potential of ChatGPT, virtual reality, human capital management systems, robotic process automation, and social media. International Entrepreneurship Review, 9(2), 7–18.
DOI: https://doi.org/10.15678/IER.2023.0902.01
View in Google Scholar
Kutnjak, A. (2021). Covid-19 accelerates digital transformation in industries: Challenges, issues, barriers and problems in transformation. IEEE Access, 9, 79373–79388.
DOI: https://doi.org/10.1109/ACCESS.2021.3084801
View in Google Scholar
Lacová, Z., Kuraková,I., Horehájová, M., & Vallušová, A. (2022). How is digital exclusion manifested in the labour market during the COVID-19 pandemic in Slovakia? Forum Scientiae Oeconomica, 10(2), 129–151.
View in Google Scholar
Laker, B., & Roulet, T. (2021). How organizations can promote employee wellness, now and post-pandemic. MIT Sloan Management Review. Retrieved from https://centaur.reading.ac.uk/94575/ (2.04.2023).
View in Google Scholar
Lane, M., & Saint-Martin, A. (2021). The impact of artificial intelligence on the labour market: What do we know so far? OECD Social, Employment and Migration Working Papers, 256. doi: 10.1787/7c895724-en.
DOI: https://doi.org/10.1787/7c895724-en
View in Google Scholar
Laplane, A., & Mazzucato, M. (2020). Socializing the risks and rewards of public investments: Economic, policy, and legal issues. Research Policy, 49(Supplement), 100008. doi: 10.1016/j.repolx.2020.100008.
DOI: https://doi.org/10.1016/j.repolx.2020.100008
View in Google Scholar
Lazaroiu, G., Androniceanu, A., Grecu, I., Grecu, G., & Neguriță, O. (2022a). Artificial intelligence-based decision-making algorithms, Internet of Things sensing networks, and sustainable cyber-physical management systems in big data-driven cognitive manufacturing. Oeconomia Copernicana, 13(4), 1047–1080.
DOI: https://doi.org/10.24136/oc.2022.030
View in Google Scholar
Lăzăroiu, G., Andronie, M., Iatagan, M., Geamănu, M., Ștefănescu, R., & Dijmărescu, I. (2022b). Deep learning-assisted smart process planning, robotic wireless sensor networks, and geospatial big data management algorithms in the Internet of Manufacturing Things. ISPRS International Journal of Geo-Information, 11, 277.
DOI: https://doi.org/10.3390/ijgi11050277
View in Google Scholar
Lee, D. S., & Chang, K. A. (2020). Industrial human resource management optimization based on skills and characteristics. Computers & Industrial Engineering, 144, 106463. doi: 10.1016/j.cie.2020.106463.
DOI: https://doi.org/10.1016/j.cie.2020.106463
View in Google Scholar
Li, C., Zhang, Y., Niu, X., Chen, F., & Zhou, H. (2023). Does artificial intelligence promote or inhibit on-the-job learning? Human reactions to AI at work. Systems, 11(3), 114.
DOI: https://doi.org/10.3390/systems11030114
View in Google Scholar
Li, L. (2018). China s manufacturing locus in 2025: With a comparison of “Made-in-China” and “Industry 4.0.”. Technological Forecasting and Social Change, 135, 66–74.
DOI: https://doi.org/10.1016/j.techfore.2017.05.028
View in Google Scholar
Li, L. (2020). Education supply chain in the era of Instustry 4.0. System Research and Behavioral Science, 37(4), 579–592.
DOI: https://doi.org/10.1002/sres.2702
View in Google Scholar
Li, L. (2022). Reskilling and upskilling the future-ready workforce for Industry 4.0 and Beyond. Information Systems Frontiers, 13, 1–16.
DOI: https://doi.org/10.1007/s10796-022-10308-y
View in Google Scholar
Lipták, K., Horváthné Csolák, E., & Musinszki, Z. (2023). The digital world and atypical work: Perceptions and difficulties of teleworking in Hungary and Romania. Human Technology, 19(1), 5–22.
DOI: https://doi.org/10.14254/1795-6889.2023.19-1.2
View in Google Scholar
Liu, N., Xu, Z., & Skare, M. (2021). The research on COVID-19 and economy from 2019 to 2020: Analysis from the perspective of bibliometrics. Oeconomia Copernicana, 12(2), 217–268.
DOI: https://doi.org/10.24136/oc.2021.009
View in Google Scholar
Livingstone, S., Mascheroni, G., & Stoilova, M. (2023). The outcomes of gaining digital skills for young people’s lives and wellbeing: A systematic evidence review. New Media & Society, 25(5), 1176–1202.
DOI: https://doi.org/10.1177/14614448211043189
View in Google Scholar
Magnavita, N., Tripepi, G., & Di Prinzio R. R. (2020). Symptoms in health care workers during the COVID-19 epidemic. A cross-sectional survey. International Journal of Environmental Research and Public Health, 17(14), 5218.
DOI: https://doi.org/10.3390/ijerph17145218
View in Google Scholar
Malik, N., Kar, A., & Gupta, S. (2021). Impact of artificial intelligence on employees working in Industry 4.0 led organizations. International Journal of Manpower, 43(2), 334–354.
DOI: https://doi.org/10.1108/IJM-03-2021-0173
View in Google Scholar
Mantello, P., & Ho, M. T. (2023). Emotional AI and the future of wellbeing in the post-pandemic workplace. AI & Society. Advance online publication.
DOI: https://doi.org/10.1007/s00146-023-01639-8
View in Google Scholar
Marcu M. R. (2021). The impact of the COVID-19 pandemic on the banking sector. Management Dynamics in the Knowledge Economy, 9(2), 205–223.
View in Google Scholar
Marques Santos, A., Barbero, J., Salotti, S., & Conte, A. (2023). Job creation and destruction in the digital age: Assessing heterogeneous effects across European Union countries. Economic Modelling, 126, 106405.
DOI: https://doi.org/10.1016/j.econmod.2023.106405
View in Google Scholar
Martela, F., & Sheldon, K. M. (2019). Clarifying the concept of well-being: Psychological need satisfaction as the common core connecting eudaimonic and subjective well-being. Review of General Psychology, 23(4), 458–474.
DOI: https://doi.org/10.1177/1089268019880886
View in Google Scholar
Mazzucato, M., & Kattel, R. (2020). COVID-19 and public-sector capacity. Oxford Review of Economic Policy, 36(Supplement_1), S256-S269.
DOI: https://doi.org/10.1093/oxrep/graa031
View in Google Scholar
Mihalca, L., Lucia Ratiu, L., Brendea, G., Metz, D., Dragan, M., & Dobre, F. (2021). Exhaustion while teleworking during COVID-19: A moderated-mediation model of role clarity, self-efficacy, and task interdependence. Oeconomia Copernicana, 12(2), 269–306.
DOI: https://doi.org/10.24136/oc.2021.010
View in Google Scholar
Mishchuk, H., Bilan, Y., & Mishchuk, V. (2023). Employment risks under the conditions of the Covid-19 pandemic and their impact on changes in economic behaviour. Entrepreneurial Business and Economics Review, 11(2), 201–216.
DOI: https://doi.org/10.15678/EBER.2023.110211
View in Google Scholar
Mitchell, T., & Brynjolfsson, E. (2017). Track how technology is transforming work. Nature, 544, 7650, 290–292.
DOI: https://doi.org/10.1038/544290a
View in Google Scholar
Mittal, P. (2020). Impact of digital capabilities and technology skills on effectiveness of government in public services. In 2020 international conference on data analytics for business and industry: Way towards a sustainable economy (ICDABI) (pp. 1–5). Bahrain: Sakheer.
DOI: https://doi.org/10.1109/ICDABI51230.2020.9325647
View in Google Scholar
Molino, M., Ingusci, E., Signore, F., Manuti, A., Giancaspro, M. L., Russo, V., Zito, M., & Cortese, C. G. (2020). Wellbeing costs of technology use during Covid-19 remote working: An investigation using the Italian translation of the technostress creators scale. Sustainability, 12(15), 5911.
DOI: https://doi.org/10.3390/su12155911
View in Google Scholar
Morandini, S., Fraboni, F., De Angelis, M., Puzzo, G., Giusino, D., & Pietrantoni, L. (2023). The impact of artificial intelligence on workers’ skills: Upskilling and reskilling in organisations. Informing Science, 26, 39–68.
DOI: https://doi.org/10.28945/5078
View in Google Scholar
Morozevich, E. S., Korotkikh, V. S., & Kuznetsova, Y. A. (2022). The development of a model for a personalized learning path using machine learning methods. Business Informatics, 16(2), 21–35.
DOI: https://doi.org/10.17323/2587-814X.2022.2.21.35
View in Google Scholar
Mortazavi, S. A. R., Mortazavi, S. M. J., & Parsaei, H. (2020). COVID-19 pandemic: How to use artificial intelligence to choose non-vulnerable workers for positions with the highest possible levels of exposure to the novel coronavirus. Journal of Biomedical Physical Engineering, 1(10), 383–386.
DOI: https://doi.org/10.31661/jbpe.v0i0.2004-1106
View in Google Scholar
Nagy, M., Lăzăroiu, G., & Valaskova, K. (2023). Machine intelligence and autonomous robotic technologies in the corporate context of SMEs: Deep learning and virtual simulation algorithms, cyber-physical production networks, and Industry 4.0-based manufacturing systems. Applied Sciences, 13, 1681.
DOI: https://doi.org/10.3390/app13031681
View in Google Scholar
Nemțeanu, M. S., Pop, R. A., Dinu, V., & Dabija, D. C. (2022). Predicting job satisfaction and work engagement behavior in the COVID-19 pandemic: A conservation of resources theory approach. Ekonomie a Management, 25(2), 23–40.
DOI: https://doi.org/10.15240/tul/001/2022-2-002
View in Google Scholar
Nemțeanu, S. M., Dabija, D. C., & Stanca, L. (2021). The influence of teleworking on performance and employee‘s counterproductive behaviour. Amfiteatru Economic, 23(58), 601–619.
DOI: https://doi.org/10.24818/EA/2021/58/601
View in Google Scholar
Nier, R. D. J, Wahab, S. N., & Daud, D. (2020). A qualitative case study on the use of drone technology for stock take activity in a third-party logistics firm in Malaysia. IOP Conference Series: Materials Scienceand Engineering, 780(6), 062014.
DOI: https://doi.org/10.1088/1757-899X/780/6/062014
View in Google Scholar
Nübler, I. (2016). New technologies: A jobless future or golden age of job creation. International Labour Office Research Department Working Paper, 13, 22–23.
View in Google Scholar
OECD (2017). Future of work and skills. 2nd meeting of the G20 Employment Working Group. Hamburg: OECD.
View in Google Scholar
OECD (2019). The economy of well-being creating opportunities for people’s well-being and economic growth. SDD Working Paper, 102.
View in Google Scholar
OECD (2021). Future of work, artificial intelligence and employment. New evidence from occupations most exposed to AI. Retrieved from https://www.oecd.org/fu ture-of-work/reports-and-data/AI-Employment-brief-2021.pdf (8.06.2023).
View in Google Scholar
Oravec, J. A. (2022). The emergence of “truth machines”?: Artificial intelligence approaches to lie detection. Ethics and Information Technology, 24(6), 1–10.
DOI: https://doi.org/10.1007/s10676-022-09621-6
View in Google Scholar
Pagán-Castaño, E., Maseda-Moreno, A., & Santos-Rojo, C. (2020). Wellbeing in work environments. Journal of Business Research, 115, 469–474.
DOI: https://doi.org/10.1016/j.jbusres.2019.12.007
View in Google Scholar
Palumbo, R. (2020). Let me go to the office! An investigation into the side effects of working from home on work-life balance. International Journal of Public Sector Management, 33(6-7), 771–790.
DOI: https://doi.org/10.1108/IJPSM-06-2020-0150
View in Google Scholar
Pap, J., Mako, C., Illessy, M., Dedaj, Z., Ardabili, S., Torok, B., & Mosavi, A. (2022a). Correlation analysis of factors affecting firm performance and employees wellbeing: Application of advanced machine learning analysis. Algorithms, 15, 300.
DOI: https://doi.org/10.3390/a15090300
View in Google Scholar
Pap, J., Mako, C., Illessy, M., Kis, N., Mosavi, A. (2022b). Modeling organizational performance with machine learning. Journal of Open Innovation: Technology, Market, and Complexity, 8(4), 177.
DOI: https://doi.org/10.3390/joitmc8040177
View in Google Scholar
Papagiannidis, S., Harris, J., & Morton, D. (2020). WHO led the digital transformation of your company? A reflection of IT related challenges during the pandemic. International Journal of Information Management, 55, 102166.
DOI: https://doi.org/10.1016/j.ijinfomgt.2020.102166
View in Google Scholar
Patino, A., & Naffi, N. (2023). Lifelong training approaches for the post-pandemic workforces: A systematic review. International Journal of Lifelong Education, 42(3), 249–269.
DOI: https://doi.org/10.1080/02601370.2023.2214333
View in Google Scholar
Pelau, C., Dabija, D. C., & Ene, I. (2021). What makes an AI device human-like? The role of interaction quality, empathy and perceived psychological anthropomorphic characteristics on the acceptance of artificial intelligence in the service industry. Computers in Human Behaviour, 122, 106855.
DOI: https://doi.org/10.1016/j.chb.2021.106855
View in Google Scholar
Pelle, A., & Tabajdi, G. (2021). Covid-19 and transformational megatrends in the European automotive industry: Evidence from business decisions with a Central and Eastern European focus. Entrepreneurial Business and Economics Review, 9(4), 19–33. doi: 10.15678/EBER.2021.090402.
DOI: https://doi.org/10.15678/EBER.2021.090402
View in Google Scholar
Pelly, D., Daly, M., Delaney, L., & Doyle, O. (2022). Worker stress, burnout, and wellbeing before and during the COVID-19 restrictions in the United Kingdom. Frontiers in Psychology, 13, 823080.
DOI: https://doi.org/10.3389/fpsyg.2022.823080
View in Google Scholar
Perifanis, N.-A., & Kitsios, F. (2023). Investigating the influence of artificial intelligence on business value in the digital era of strategy: A literature review. Information, 14, 85.
DOI: https://doi.org/10.3390/info14020085
View in Google Scholar
Piccialli, F., di Cola, V. S., Giampaolo, F., & Cuomo, S. (2021). The role of artificial intelligence in fighting the COVID-19 pandemic. Information Systems Frontiers, 23(6), 1467–1497.
DOI: https://doi.org/10.1007/s10796-021-10131-x
View in Google Scholar
Ping, H., & Ying, Y.G. (2018). Comprehensive view on the effect of artificial intelligence on employment. Topics In Education, Culture and Social Development, 1(1), 32–35.
DOI: https://doi.org/10.26480/ismiemls.01.2018.32.35
View in Google Scholar
Platts, K., Breckon, J., & Marshall, E. (2022). Enforced home-working under lockdown and its impact on employee wellbeing: A cross-sectional study. BMC Public Health, 22, 199.
DOI: https://doi.org/10.1186/s12889-022-12630-1
View in Google Scholar
Polychronidou, P., Zoumpoulidis, V., & Valsamidis, S. (2022). Labor digitalization Europe. Intellectual Economics, 15(2), 6–21.
View in Google Scholar
Randstad (2020). Skilling today global survey. Retrieved from https://info.risesmart. com/skilling-today-global-survey-report (17.05.2023).
View in Google Scholar
Rapanta, C., Botturi, L., Goodyear, P. Guardia, L., & Koole, M. (2021). Balancing technology, pedagogy and the new normal: Post-pandemic challenges for higher education. Postdigital Science and Education, 3, 715–742.
DOI: https://doi.org/10.1007/s42438-021-00249-1
View in Google Scholar
Russell, E., & Daniels, K. (2018). Measuring affective well-being at work using short-form scales: Implications for affective structures and participant instructions. Human Relations, 71(11), 1478–1507.
DOI: https://doi.org/10.1177/0018726717751034
View in Google Scholar
Sagar, S., Rastogi, R., Garg, V., & Basavaraddi, I. V. (2022). Impact of meditation on quality of life of employees. International Journal of Reliable and Quality E-Healthcare, 11(1), 1–16.
DOI: https://doi.org/10.4018/IJRQEH.305843
View in Google Scholar
Saleem, F., Malik, M. I., & Qureshi, S. S. (2021). Work stress hampering employee performance during COVID-19: Is safety culture needed? Frontiers in Psychology, 26(12), 655839.
DOI: https://doi.org/10.3389/fpsyg.2021.655839
View in Google Scholar
Saman, E. N., Ghulam, A., Contreras, F., & Aldeanueva, F. I. (2022). Work–family and family–work conflict and stress in times of COVID-19. Frontiers in Psychology, 13, 951149.
DOI: https://doi.org/10.3389/fpsyg.2022.951149
View in Google Scholar
Saxena, A., & Gautam, S. S. (2021). Employee mental well-being amidst Covid-19: Major stressors and distress. Journal of Public Affairs, 21(3), e2552.
DOI: https://doi.org/10.1002/pa.2552
View in Google Scholar
Schwab, K., & Zahidi, S. (2020). The future of jobs report 2020. World Economic Forum, October. Retrieved from https://www3.weforum.org/docs/WEF_Futu re_of_Jobs_2020.pdf (17.04.2023).
View in Google Scholar
Semaan, J., Underwood, J., & Hyde, J. (2021). An investigation of work-based education and training needs for effective BIM adoption and implementation: An organisational upskilling model. Applied Science, 11, 8646.
DOI: https://doi.org/10.3390/app11188646
View in Google Scholar
Simonetti, I., Belloni, M., Farina, E., & Zantomio, F. (2022). Labour market institutions and long-term adjustments to health shocks: Evidence from Italian administrative records. Labour Economics, 79(C), 102277.
DOI: https://doi.org/10.1016/j.labeco.2022.102277
View in Google Scholar
Song, Y., & Gao, J. (2020). Does telework stress employees out? A study on working at home and subjective well-being for wage/salary workers. Journal of Happiness Studies, 21, 26490–2668.
DOI: https://doi.org/10.1007/s10902-019-00196-6
View in Google Scholar
Sonnentag, S., Tay, L., & Nesher Shoshan, H. (2023). A review on health and well-being at work: More than stressor sand strains. Personnel Psychology, 76, 473–510.
DOI: https://doi.org/10.1111/peps.12572
View in Google Scholar
Soto-Acosta, P. (2020). COVID-19 pandemic: Shifting digital transformation to a high-speed gear. Information Systems Management, 37(4), 260–266.
DOI: https://doi.org/10.1080/10580530.2020.1814461
View in Google Scholar
Stamate, A. N., Sauvé, G., & Denis, P. L. (2021). The rise of the machines and how they impact workers' psychological health: An empirical study. Human Behavior and Emerging Technologies, 3(5), 942–955.
DOI: https://doi.org/10.1002/hbe2.315
View in Google Scholar
Strack, R., Carrasco, M., Kolo, P., Nouri, N., Priddis, M., & George, R. (2021). The future of jobs in the era of AI. Boston Consulting Group. Retrieved from https://web-assets.bcg.com/f5/e7/9aa9f81a446198ac5402aaf97a87/bcg-the-future-of-jobs-in-the-era-of-ai-mar-2021-r-r.pdf (5.06.2023).
View in Google Scholar
Suhasini, B., Santhosh, L., & Kumar, N. (2020). Emerging trends and future perspective of human resource reskilling in higher education. International Journal of Recent Technology and Engineering, 8(2S4), 351–353.
DOI: https://doi.org/10.35940/ijrte.B1067.0782S419
View in Google Scholar
Swarajya, L. P., Reddy, A. M., Yarlagadda, S., Yarlagadda, S., & Akkineni, H. (2021). An extensive analytical approach on human resources using random forest algorithm. International Journal of Engineering Trends and Technology, 69(5), 119–127.
DOI: https://doi.org/10.14445/22315381/IJETT-V69I5P217
View in Google Scholar
Thern, E., de Munter, J., Hemmingsson, T., & Rasmussen, F. (2017). Long-term effects of youth unemployment on mental health: Does an economic crisis make a difference? Journal of Epidemiological Community Health, 71(4), 344–349.
DOI: https://doi.org/10.1136/jech-2016-208012
View in Google Scholar
Tinmaz, H., Lee, Y. T., Fanea-Ivanovici, M., & Baber, H. (2022). A systematic review on digital literacy. Smart Learning Environment, 9, 21.
DOI: https://doi.org/10.1186/s40561-022-00204-y
View in Google Scholar
Tronco-Hernández, Y. A., Parente, F., Faghy, M. A., Roscoe, C. M. P., Maratos, F. A. (2021). Influence of the COVID-19 lockdown on the physical and psychosocial well-being and work productivity of remote workers: Cross-sectional correlational study. JMIRx Med, 2(4), e30708.
DOI: https://doi.org/10.2196/30708
View in Google Scholar
Ulfert, A. S., Antoni, C. H., & Ellwart, T. (2022). The role of agent autonomy in using decision support systems at work. Computers in Human Behavior, 126, 106987.
DOI: https://doi.org/10.1016/j.chb.2021.106987
View in Google Scholar
UN (2023). WHO announced the end of COVID-19 – Pandemic. Retrieved from https://news.un.org/en/story/2023/05/1136367 (10.06.2023).
View in Google Scholar
Valaskova, K., Nagy, M., Zabojnik, S., & Lăzăroiu, G. (2022). Industry 4.0 wireless networks and cyber-physical smart manufacturing systems as accelerators of value-added growth in Slovak exports. Mathematics, 10, 2452.
DOI: https://doi.org/10.3390/math10142452
View in Google Scholar
van Eck, N. J., & Waltman, L. (2023). VOS Viewer Instructions. VOS Viewer. Retrieved from https://www.vosviewer.com/documentation/Manual_VOSviewer_ 1.6.19.pdf (17.06.2023).
View in Google Scholar
Van Horn, J. E., Taris, T. W., Schaufeli, W. B., & Schreurs, P. J. G. (2004). The structure of occupational well-being: A study among Dutch teachers. Journal of Occupational Organizational Psychology, 77(3), 365–375.
DOI: https://doi.org/10.1348/0963179041752718
View in Google Scholar
Van Laar, E., Van Deursen, A. J. A. M., Van Dijk, J. A. G. M., & de Haan, J. (2017). The relation between 21st-century skills and digital skills: A systematic literature review. Computers in Human Behavior, 72, 577–588.
DOI: https://doi.org/10.1016/j.chb.2017.03.010
View in Google Scholar
Van Laar, E., van Deursen, A. J. A. M., van Dijk, J. A. G. M., & de Haan, J. (2020). Determinants of 21st-century skills and 21st-century digital skills for workers: A systematic literature review. Sage Open, 10(1).
DOI: https://doi.org/10.1177/2158244019900176
View in Google Scholar
Vks, O., Sarwar, A., & Pervez, N. (2022). The study of mindfulness as an intervening factor for enhanced psychological well-being in building the level of resilience. Frontiers in Psychology, 13, 1056834.
DOI: https://doi.org/10.3389/fpsyg.2022.1056834
View in Google Scholar
Vyas, L. (2022). New normal” at work in a post-COVID world: Work–life balance and labor markets. Policy and Society, 41(1), 155–167.
DOI: https://doi.org/10.1093/polsoc/puab011
View in Google Scholar
Wach, K., Duong, C. D., Ejdys, J., Kazlauskaitė, R., Korzynski, P., Mazurek, G., Paliszkiewicz, J., & Ziemba, E. (2023). The dark side of generative artificial intelligence: A critical analysis of controversies and risks of ChatGPT. Entrepreneurial Business and Economics Review, 11(2), 7–30.
DOI: https://doi.org/10.15678/EBER.2023.110201
View in Google Scholar
Wahab, S. N., Rajendran, S. D., & Yeap, S. P. (2021). Upskilling and reskilling requirement in logistics and supply chain industry for the 4th Industrial Revolution. LogForum. Scientific Journal of Logistics, 17(3), 399–410.
DOI: https://doi.org/10.17270/J.LOG.2021.606
View in Google Scholar
WEF (2021). The great resignation. World Economic Forum. Retrieved from https://www.weforum.org/agenda/2021/11/what-is-the-great-resignation-and-w hat-can-we-learn-from-it (10.04.2023).
View in Google Scholar
WEF (2022). The future of jobs. World Economic Forum. Retrieved from https://www3.weforum.org/docs/WEF_Future_of_Jobs_2020.pdf (10.04.2023).
View in Google Scholar
Weziak-Bialowolska, D., Bialowolski, P., Sacco, P. L., VanderWeele, T. J., & McNeely, E. (2020). Well-being in life and well-being at work: Which comes first? Evidence from a longitudinal study. Frontiers in Public Health, 8, 103.
DOI: https://doi.org/10.3389/fpubh.2020.00103
View in Google Scholar
Weziak-Bialowolska, D., Bialowolski, P., VanderWeele, T. J., & McNeely, E. (2021). Character strengths involving an orientation to promote good can help your health and well-being. Evidence from two longitudinal studies. American Journal of Health Promotion, 35(3), 388–398.
DOI: https://doi.org/10.1177/0890117120964083
View in Google Scholar
Woods, R., Doherty, O., & Stephens, S. (2022). Technology driven change in the retail sector: Implications for higher education. Industry and Higher Education, 36(2), 128–137.
DOI: https://doi.org/10.1177/09504222211009180
View in Google Scholar
Wu, G., Wu, Y., Li, H., & Dan, C. (2018). Job burnout, work-family conflict and project performance for construction professionals: The moderating role of organizational support. International Journal of Environmental Research and Public Health, 15(12), 2869.
DOI: https://doi.org/10.3390/ijerph15122869
View in Google Scholar
Zhang, D., & Pan, J., (2022). An intelligent scheduling model of computer human resources in complex scenarios based on artificial intelligence. Wireless Communications and Mobile Computing, 8546634.
DOI: https://doi.org/10.1155/2022/8546634
View in Google Scholar
Zhou, M., Wang, D., Zhou, L., Liu, Y., & Hu, Y. (2021). The effect of work-family conflict on occupational well-being among primary and secondary school teachers: The mediating role of psychological capital. Frontiers in Public Health, 9, 745118.
DOI: https://doi.org/10.3389/fpubh.2021.745118
View in Google Scholar
Żur, A., & Wałęga, A. (2023). Internationalization and innovation orientation as factors of employee learning and development adaptation during Covid-19: Evidence from Polish SMEs. Entrepreneurial Business and Economics Review, 11(1), 77–91.
DOI: https://doi.org/10.15678/EBER.2023.110104
View in Google Scholar