Czasopismo
Tytuł artykułu
Warianty tytułu
Języki publikacji
Abstrakty
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 wellbeing, 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. (original abstract)
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 wellbeing, 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. (original abstract)
Twórcy
autor
- Babeș-Bolyai University Cluj-Napoca, Romania
autor
- Babeș-Bolyai University Cluj-Napoca, Romania
autor
- Babeș-Bolyai University, Cluj-Napoca, Romania
Bibliografia
- 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. doi: 10.22034/ijism.2022.1977763.0.
- 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: 10.3390/su14148539.
- 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: 10.1088/1742-6596/1840/1/012040.
- 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. doi: 10.1080/09585192.2021.1891114.
- 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: 10.1177/21582440221096142.
- Allen, M. (2022). Trainer upskilling and reskilling models in business education. BW Academic Journal, 1(1), 127-131.
- 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: 10.3390/ijgi12020035.
- 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: 10.3390/electronics10202497.
- 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: 10.3390/electronics12010022.
- 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: 10.1108/VJIKMS-09-2021-0203.
- 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: 10.24818/EA/2021/57/548.
- 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: 10.1108/IJOPM-01-2022-0069.
- 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: 10.1007/s11205-023-03109-6.
- 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: 10.3390/su14010294.
- 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: 10.1108/IJM-10-2019-0458.
- Bilal, H., & Varallyai, L. (2019). Will artificial intelligence take over human resources: Recruitment and selection? Network Intelligence Studies, 13, 21-30.
- Bjursell, C., Bergmo-Prvulovic, I., & Hedegaard, J. (2021). Telework and lifelong learning. Frontiers in Sociology, 6, 642277. doi: 10.3389/fsoc.2021.642277.
- Brun-Schammé, A., & Rey, M. (2021). A new approach to skills mismatch. OECD Productivity Working Papers, 24.
- Bruun, E., & Duka, A. (2018). Artificial intelligence, jobs and the future of work: Racing with the machines. Basic Income Studies, 13(2), 20180018. doi: 10.1515/bis-2018-0018.
- 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: 10.1108/JTF-07-2020-0114.
- 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: 10.1016/j.jbusres.2022.08.019.
- 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-future-is-now-closing-the-skills-gap-in-europes-public-sector (2.05.2023).
- 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: 10.1108/EJTD-06-2022-0062.
- 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: 10.1111/peps.12556.
- Daniels, K. (2000). Measures of five aspects of affective well-being at work. Human Relations, 53(2), 275-294. doi: 10.1177/0018726700532005.
- Davenport, T. H., & Mittal, N. (2023). How companies can prepare for the coming "AI-first" world. Strategy & Leadership, 51(1), 26-30. doi: 10.1108/SL-11-2022-0107.
- 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: 10.3390/su12156086.
- 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: 10.1007/978-3-030-19875-6_17.
- Dicuonzo, G., Donofrio, F., Fusco, A., & Shini, M. (2023). Healthcare system: Moving forward with artificial intelligence. Technovation, 120, 102510. doi: 10.1016/j.technovation.2022.102510.
- 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: 10.1177/10242589221143044
- 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: 10.3390/electronics11213483.
- Dosi, G., Piva, M., Virgillito, M. E., & Vivarelli, M. (2021). Embodied and disembodied technological change: The sectoral patterns of job-creation and jobdestruction. Research Policy, 50(4), 104199. doi: 10.1016/j.respol.2021.104199.
- 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. doi: 10.25103/ijbesar.103.03.
- 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: 10.3390/su14031718.
- 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: 10.1007/s11482-022-10113-4.
- 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.
- 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: 10.3390/su15010360.
- Foa, R., Gilbert, S., & Fabian, M. O. (2020). COVID-19 and subjective well-being: Separating the effects of lockdowns from the pandemic. SSRN. doi: 10.2139/ssrn.3674080.
- 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: 10.1016/j.techfore.2022.121716.
- Graetz, G. (2020). Labor demand in the past, present, and future. IZA Discussion Paper, 13142.
- 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: 10.3390/su13094975.
- 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.
- 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: 10.28991/esj-2021-SPER-03.
- Hemin, Q. (2018). Will artificial intelligence brighten or threaten the future. MNSES9100 - Science, ethics and society. Retrieved from https://www.researchgate.net/publication/323535179_Will_Artificial_ Intelligence_Brighten_or_Threaten_the_Future (17.04.2023).
- Henderikx, M., & Stoffers, J. (2022). An exploratory literature study into digital transformation and leadership: Toward future-proof middle managers. Sustainability, 14(2), 687. doi: 10.3390/su14020687.
- 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: 10.1108/JOSM-05-2019-0160.
- 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: 10.18502/kss.v5i9.9887.
- 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: 10.1108/WHATT-01-2023-0011.
- 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.
- ILO (2022). World employment and social outlook, trends 2022. International Labour Organization.
- 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: 10.1080/09585192.2021.189111.
- 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: 10.2478/sbe-2021-0008.
- 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: 10.1016/j.jjimei.2022.100107.
- 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: 10.1007/978-3-030-52575-0_82.
- Jiang, F., Wang, L., Li, J.-X., & Liu, J. (2022). How smart technology affects the wellbeing and supportive learning performance of logistics employees? Frontiers in Psychology, 12, 768440. doi: 10.3389/fpsyg.2021.768440.
- 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: 10.34135/sjps.200204.
- 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: 10.1007/s12144-023-04250-8
- 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: 10.1177/2164956120982956
- 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: 10.1002/isaf.1503.
- 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: 10.26803/ijlter.19.7.4
- 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: 10.3390/su13094786.
- Khogali, H., & Mekid, S. (2022). The blended future of automation and AI: Examining some long-term societal impact features. SSRN. doi: 10.2139/ssrn.4239580.
- 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: 10.1007/978-3-031-09070-7_60.
- 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: 10.15678/IER.2023.0902.01
- Kutnjak, A. (2021). Covid-19 accelerates digital transformation in industries: Challenges, issues, barriers and problems in transformation. IEEE Access, 9, 79373- 79388. doi: 10.1109/ACCESS.2021.3084801.
- 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. doi: 10.23762/FSO_VOL10_NO2_7.
- 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).
- 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.
- 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.
- 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 datadriven cognitive manufacturing. Oeconomia Copernicana, 13(4), 1047-1080. doi: 10.24136/oc.2022.030
- 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: 10.3390/ijgi11050277.
- 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.
- 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: 10.3390/systems11030114.
- 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: 10.1016/j.techfore.2017.05.028.
- Li, L. (2020). Education supply chain in the era of Instustry 4.0. System Research and Behavioral Science, 37(4), 579-592. doi: 10.1002/sres.2702.
- Li, L. (2022). Reskilling and upskilling the future-ready workforce for Industry 4.0 and Beyond. Information Systems Frontiers, 13, 1-16. doi: 10.1007/s10796-022-10308-y.
- 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: 10.14254/1795-6889.2023.19-1.2.
- 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: 10.24136/oc.2021.009.
- 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: 10.1177/14614448211043189.
- 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: 10.3390/ijerph17145218.
- 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: 10.1108/IJM-03-2021-0173.
- Mantello, P., & Ho, M. T. (2023). Emotional AI and the future of wellbeing in the post-pandemic workplace. AI & Society. Advance online publication. doi: 10.1007/s00146-023-01639-8.
- 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. doi: 10.2478/mdke-2021-0013.
- 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: 10.1016/j.econmod.2023.106405.
- 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: 10.1177/1089268019880886.
- Mazzucato, M., & Kattel, R. (2020). COVID-19 and public-sector capacity. Oxford Review of Economic Policy, 36(Supplement_1), S256-S269. doi: 10.1093/oxrep/graa031.
- 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: 10.24136/oc.2021.010.
- 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: 10.15678/EBER.2023.110211.
- Mitchell, T., & Brynjolfsson, E. (2017). Track how technology is transforming work. Nature, 544, 7650, 290-292.
- 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: 10.1109/ICDABI51230.2020.9325647.
- 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: 10.3390/su12155911.
- 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: 10.28945/5078.
- 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: 10.17323/2587-814X.2022.2.21.35.
- 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: 10.31661/jbpe.v0i0.2004-1106.
- 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: 10.3390/app13031681.
- 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: 10.15240/tul/001/2022-2-002
- 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: 10.24818/EA/2021/58/601.
- 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: 10.1088/1757-899x/780/6/062014.
- Nübler, I. (2016). New technologies: A jobless future or golden age of job creation. International Labour Office Research Department Working Paper, 13, 22-23.
- OECD (2017). Future of work and skills. 2nd meeting of the G20 Employment Working Group. Hamburg: OECD.
- OECD (2019). The economy of well-being creating opportunities for people's wellbeing and economic growth. SDD Working Paper, 102.
- 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).
- Oravec, J. A. (2022). The emergence of "truth machines"?: Artificial intelligence approaches to lie detection. Ethics and Information Technology, 24(6), 1-10. doi: 10.1007/s10676-022-09621-6.
- Pagán-Castano, E., Maseda-Moreno, A., & Santos-Rojo, C. (2020). Wellbeing in work environments. Journal of Business Research, 115, 469-474. doi: 10.1016/j.jbus res.2019.12.007.
- 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: 10.1108/IJPSM-06-2020-0150.
- 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: 10.3390/a15090300.
- 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: 10.3390/joitmc8040177.
- 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: 10.1016/j.ijinfomgt.2020.102166.
- 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: 10.1080/02601370.2023.2214333.
- 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: 10.1016/j.chb.2021.106855.
- 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.
- 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: 10.3389/fpsyg.2022.823080.
- 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: 10.3390/info14020085.
- 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: 10.1007/s10796-021-10131-x.
- 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: 10.26480/ismiemls.01.2018.32.35.
- 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: 10.1186/s12889-022-12630-1.
- Polychronidou, P., Zoumpoulidis, V., & Valsamidis, S. (2022). Labor digitalization Europe. Intellectual Economics, 15(2), 6-21. doi: 10.13165/IE-21-15-2-01.
- Randstad (2020). Skilling today global survey. Retrieved from https://info.risesmart.com/skilling-today-global-survey-report (17.05.2023).
- 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: 10.1007/s42438-021-00249-1.
- 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: 10.1177/0018726717751034.
- 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: 10.4018/IJRQEH.305843.
- 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: 10.3389/fpsyg.2021.655839.
- 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: 10.3389/fpsyg.2022.951149.
- 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: 10.1002/pa.2552.
- 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).
- 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: 10.3390/app11188646.
- 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: 10.1016/j.labeco.2022.102277.
- 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: 10.1007/s10902-019-00196-6.
- Sonnentag, S., Tay, L., & Nesher Shoshan, H. (2023). A review on health and wellbeing at work: More than stressor sand strains. Personnel Psychology, 76, 473-510. doi: 10.1111/peps.12572
- Soto-Acosta, P. (2020). COVID-19 pandemic: Shifting digital transformation to a high-speed gear. Information Systems Management, 37(4), 260-266. doi: 10.1080/10580530.2020.1814461.
- 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: 10.1002/hbe2.315
- 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-futureof-jobs-in-the-era-of-ai-mar-2021-r-r.pdf (5.06.2023).
- 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: 10.35940/ijrte.b1067.0782s419.
- 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: 10.14445/22315381/IJETT-V69I5P217
- 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: 10.1136/jech-2016-208012.
- Tinmaz, H., Lee, Y. T., Fanea-Ivanovici, M., & Baber, H. (2022). A systematic review on digital literacy. Smart Learning Environment, 9, 21. doi: 10.1186/s40561-022-00204-y
- 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: 10.2196/30708
- 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: 10.1016/j.chb.2021.106987.
- UN (2023). WHO announced the end of COVID-19 - Pandemic. Retrieved from https://news.un.org/en/story/2023/05/1136367 (10.06.2023).
- 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: 10.3390/math10142452.
- 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).
- 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: 10.1348/0963179041752718.
- 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: 10.1016/j.Chb.2017.03.010.
- 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: 10.1177/2158244019900176.
- 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: 10.3389/fpsyg.2022.1056834.
- 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: 10.1093/polsoc/puab011.
- 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: 10.15678/EBER.2023.110201.
- 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: 10.17270/J.LOG.2021.606.
- WEF (2021). The great resignation. World Economic Forum. Retrieved from https://www.weforum.org/agenda/2021/11/what-is-the-great-resignation-and-what-can-we-learn-from-it (10.04.2023).
- 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).
- 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: 10.3389/fpubh.2020.00103.
- 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: 10.1177/0890117120964083.
- 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: 10.1177/09504222211009180.
- 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: 10.3390/ijerph15122869.
- 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: 10.1155/2022/8546634.
- 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: 10.3389/fpubh.2021.745118.
- 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: 10.15678/EBER.2023.110104a.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171677347