Generation Z and artificial intelligence – research directions in management science: a scientometric analysis based on Scopus

Ławińska, Olga; Korombel, Anna

doi:10.21008/j.0239-9415.2025.091.12

Artykuł - szczegóły

Tytuł artykułu

Generation Z and artificial intelligence – research directions in management science: a scientometric analysis based on Scopus

Autorzy

Ławińska Olga , Korombel Anna

Treść / Zawartość

Pełne teksty:

Pobierz

Identyfikatory

DOI

10.21008/j.0239-9415.2025.091.12

Warianty tytułu

Pokolenie Z i sztuczna inteligencja - kierunki badań w naukach o zarządzaniu: analiza scjentometryczna na podstawie Scopus

Języki publikacji

Abstrakty

The aim of the article is to identify current and emerging research directions in management science in the area of topics linking artificial intelligence and Generation Z. The article uses scientometric analysis. Publications included in the Scopus database combining the topics of artificial intelligence and Generation Z in the area of management sciences were analysed. VOSviewer software was used for scientometric analysis. The study covered the period from 2018 to 2025 (up to and including 9.04.2025). The most frequent keywords were „social media” and „technology acceptance model”. Through a combination of cluster analysis and critical review, five research themes were identified in each cluster: Gen Z’s intention to use a technology, influenced by perceived ease of use; Generation Z’s trust and satisfaction in the use of AI tools; the impact of using augmented reality, artificial intelligence-enabled chatbots, and social media on the purchasing behaviour of Generation Z; robot implementation during the COVID-19 pandemic, often focusing on comparing customer experiences across Generations X, Y and Z; the adaptation of metaverse and service robots technologies by Generation Z in a virtual reality. The authors also identified emerging research directions. The first of them is the use of psychology theory in understanding Generation Z’s attitudes towards AI (in different relationship spheres, e.g. work, school). The second is the study of Generation Z representatives’ relationships with other users in a virtual metaverse, understood as a digital space where users can interact with other users.

Celem artykułu jest identyfikacja obecnych i wyłaniających się kierunków badań w naukach o zarządzaniu w obszarze tematyki łączącej sztuczną inteligencję i pokolenie Z. W pracy wykorzystano analizę scjentometryczną. Analizie zostały poddane publikacje zamieszczone w bazie danych Scopus łączące tematykę sztucznej inteligencji i pokolenia Z w obszarze nauk o zarządzaniu. Do analizy scientometrycznej wykorzystano oprogramowanie VOSviewer. Badaniem objęto okres od 2018 do 2025 r. (do 9.04.2025 włącznie). Najczęściej pojawiającymi się słowami kluczowymi były „media społecznościowe” i „model akceptacji technologii”. Dzięki połączeniu analizy klastrów i krytycznego przeglądu zidentyfikowano pięć tematów badawczych w poszczególnych klastrach: zamiar korzystania z technologii przez pokolenie Z pod wpływem postrzeganej łatwości użytkowania; zaufanie i satysfakcja pokolenia Z z korzystania z narzędzi sztucznej inteligencji; wpływ korzystania z rzeczywistości rozszerzonej, chatbotów obsługujących sztuczną inteligencję i mediów społecznościowych na zachowania zakupowe pokolenia Z; wdrażanie robotów podczas pandemii COVID-19, często koncentrujące się na porównywaniu doświadczeń klientów między pokoleniami X, Y i Z; adaptacja technologii metawersum i robotów usługowych przez pokolenie Z w wirtualnej rzeczywistości. Autorki wskazały także wyłaniające się kierunki badań. Pierwszym z nich jest wykorzystanie teorii psychologii w zrozumieniu postaw pokolenia Z względem sztucznej inteligencji (w różnych obszarach relacji, np. w pracy, szkole). Drugim natomiast jest badanie relacji przedstawicieli pokolenia Z z innymi użytkownikami w wirtualnej przestrzeni metawersum, rozumianej jako cyfrowa przestrzeń, w której użytkownicy mogą wchodzić w interakcje z innymi użytkownikami.

Słowa kluczowe

management generation Z artificial intelligence scientometric analysis

zarządzanie sztuczna inteligencja pokolenie Z analiza scjentometryczna

Wydawca

Wydawnictwo Politechniki Poznańskiej

Czasopismo

Zeszyty Naukowe Politechniki Poznańskiej. Organizacja i Zarządzanie

Rocznik

2025

Tom

Nr 91

Strony

223--246

Opis fizyczny

Bibliogr. 57 poz., rys., tab.

Twórcy

autor

Ławińska Olga

Czestochowa University of Technology

https://orcid.org/0000-0003-1960-8211

autor

Korombel Anna

Czestochowa University of Technology

https://orcid.org/0000-0003-0546-9708

Bibliografia

1. Ameen, N., Cheah J.-H., Kumar, S. (2022). It’s all part of the customer journey: The impact of augmented reality, chatbots, and social media on the body image and self-esteem of Generation Z female consumers. Psychology & Marketing, 39(11), 2110-2129, https://doi.org/10.1002/mar.21715.
2. Ameen, N., Hosany, S., Taheri, B. (2023). Generation Z’s psychology and new‐age technologies: implications for future research. Psychology & Marketing, 40(10), 2029-2040, https://doi.org/10.1002/mar.21868.
3. Armelia Windasari, N., Kusumawati, N., Larasati, N., Paspasuci Amelia, R. (2022). Digital-only banking experience: Insights from gen Y and gen Z. Journal of Innovation & Knowledge, 7(2), 100170, https://doi.org/10.1016/j.jik.2022.100170.
4. Bassiouni, D., Hackley, C. (2014). Generation Z Children’s Adaptation to Digital Consumer Culture: A Critical Literature Review. Journal of Customer Behaviour, 13/2, 113-133, https://doi.org/10.1362/147539214X14024779483591.
5. Börner, K., Chen, C., Boyack, K.W. (2003). Visualizing knowledge domains. Annual Review of Information Science and Technology, 37(1), 179-255, https://doi.org/10.1002/aris.1440370106.
6. Bunea, O.-I., Corboș, R.-A., Mișu, S.I., Triculescu, M., Trifu, A. (2024). The Next-Generation Shopper: A Study of Generation-Z Perceptions of AI in Online Shopping. Journal of Theoretical and Applied Electronic Commerce Research, 19(4), 2605-2629, https://doi.org/10.3390/jtaer19040125.
7. D’Acunto, D., Filieri, R., Okumus, F. (2025). The Gen Z attitude-behavior gap in sustainability-framed eWOM: A generational cohort theory perspective. International Journal of Hospitality Management, 129, 104194, https://doi.org/10.1016/j.ijhm.2025.104194.
8. Dhiman, P., Kaur, A., Iwendi, C., Mohan, S.K. (2023). A scientometric analysis of deep learning approaches for detecting fake news. Electronics, 12, 948, https://doi.org/ 10.3390/electronics12040948.
9. Dimock, M. (2019). Defining generations: where Millennials end and Generation Z begins. Pew Research Center, 17(1), 1-7.
10. Dinh, CM., Park, S. (2024). How to increase consumer intention to use Chatbots? An empirical analysis of hedonic and utilitarian motivations on social presence and the moderating effects of fear across generations. Electronic Commerce Research, 24(4), 2427-2467, https://doi.org/10.1007/s10660-022-09662-5.
11. Eck, N.J. van, Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538, https://doi.org/10.1007/s11192-009-0146-3.
12. Eck, N.J. van, Waltman, L. (2023). Manual for VOSviewer version 1.6.20 (31.10.2023).
13. European Commission (2019). A definition of AI: Main capabilities and scientific disciplines. High-Level Expert Group on Artificial Intelligence. Retrieved from https://digital-strategy.ec.europa.eu/en/library/definition-artificial-intelligence-main-capabilities-and-scientific-disciplines (9.04.2025).
14. Eyerman, R., Turner, B.S. (1998). Outline of a theory of generations. The European Journal of Social Theory, 1(1), 91-106, https://doi.org/10.1177/136843198001001007.
15. Go, H., Kang, M. (2023). Metaverse tourism for sustainable tourism development: Tourism Agenda 2030. Tourism Review, 78(2), 381-394, https://doi.org/10.1108/TR-02-2022-0102.
16. Gunaseelan, S. (2023). Application of bibliometric laws in global limnology research publications: a scientometric analysis based on web of science 1989-2020. Kelpro Bulletin, 27, 93-111, https://doi.org/10.5281/zenodo.8129201.
17. Gupta, K.P., Pande, S. (2023). Understanding Generation Z consumers’ revisit intentions to robotic service restaurants. Young Consumers, 24(3), 331-351, https://doi.org/10.1108/YC-09-2022-1598.
18. Hampton, D., Keys, Y. (2017). Generation Z students: Will they change our nursing classrooms? Journal of Nursing Education and Practice, 7(4), 111-115, https://doi.org/10.5430/jnep.v7n4p111.
19. Ho, S.P.S., Chow, M.Y.C. (2024). The role of artificial intelligence in consumers’ brand preference for retail banks in Hong Kong. Journal of Financial Services Marketing, 29(2), 292-305, https://doi.org/10.1057/s41264-022-00207-3.
20. Indiran, L.A.P., Putri, A.A.S., Fu, C., Dungog, J.A. (2024). Virtual Try-On Application and Fashion Purchase Intentions Among Gen Z Consumers in Malaysia. In: T. Tarnanidis, E. Papachristou, M. Karypidis, V. Ismyrlis (eds.). Driving Green Marketing in Fashion and Retail. IGI Global Scientific Publishing, 194-212, https://doi.org/10.4018/979-8-3693-3049-4.ch012.
21. Jhawar, A., Kumar, P., Varshney, S. (2023). The emergence of virtual influencers: a shift in the influencer marketing paradigm. Young Consumers, 24(4), 468-484, https://doi.org/10.1108/YC-05-2022-1529.
22. Joshi, H. (2025). Integrating trust and satisfaction into the UTAUT model to predict Chatbot adoption – a comparison between Gen-Z and Millennials. International Journal of Information Management Data Insights, 5(1), 100332, https://doi.org/10.1016/j.jjimei.2025.100332.
23. Kamenidou, I.C., Mamalis, S.A., Pavlidis, S., Bara, E.-Z.G. (2019). Segmenting the Generation Z cohort university students based on sustainable food consumption behavior: A preliminary study. Sustainability, 11(3), 837, 1-22, https://doi.org/10.3390/su11030837.
24. Kholkina, V., Chesnokova, E., Zelenskaya, E. (2025). Virtual or human? The impact of the influencer type on Gen Z consumer outcomes. Journal of Product & Brand Management, 34(1), 104-118, https://doi.org/10.1108/JPBM-12-2023-4885.
25. Kim, S., Jang, S., Choi, W., Youn, C., Lee, Y. (2022). Contactless service encounters among Millennials and Generation Z: the effects of Millennials and Gen Z characteristics on technology self-efficacy and preference for contactless service. Journal of Research in Interactive Marketing, 16(1), 82-100, https://doi.org/10.1108/JRIM-01-2021-0020.
26. Lai, Z.J., Leong, M.K., Khoo, K.L., Sidhu, S.K. (2025). Integrating technology acceptance model and value-based adoption model to determine consumers’ perception of value and intention to adopt AR in online shopping. Asia Pacific Journal of Marketing and Logistics, 37(1), 1-19, https://doi.org/10.1108/APJML-03-2024-0386.
27. Lisboa, A., Vitorino, L., Antunes, R. (2022). Gen Zers’ intention to purchase products with sustainable packaging: an alternative perspective to the attitude-behaviour gap. Journal of Marketing Management, 38(9-10), 967-992, https://doi.org/10.1080/0267257X.2022.2083214.
28. Lissitsa, S., Kol, O. (2021). Four generational cohorts and hedonic m-shopping: association between personality traits and purchase intention. Electronic Commerce Research, 21(2), 545-570, https://doi.org/10.1007/s10660-019-09381-4.
29. Llanos-Herrera, G.R., Merigo, J.M. (2019). Overview of brand personality research with bibliometric indicators. Kybernetes, 48(3), 546-569, https://doi.org/10.1108/K-02-2018-0051.
30. McCarthy, J. (2007). What is artificial intelligence? Computer Science Department Stanford University. Retrieved from https://jmc.stanford.edu/articles/whatisai.html (9.04.2025).
31. McCrindle, M. (2025a). Gen Z and Gen Alpha Infographic Update. Retrieved from https://mccrindle.com.au/article/topic/generation-z/gen-z-and-gen-alpha-infographic-update/(4.04.2025).
32. McCrindle, M. (2025b). Welcome Gen Beta. Retrieved from https://mccrindle.com.au/article/generation-beta-defined/ (4.04.2025).
33. McKinsey (2024). What is Gen Z? Retrieved from https://www.mckinsey.com/featured-in-sights/mckinsey-explainers/what-is-gen-z#/ (12.04.2025).
34. Mahmoud, A.B., Fuxman, L., Mohr, I., Reisel, W.D., Grigoriou, N. (2021). „We aren’t your reincarnation!” workplace motivation across X, Y and Z generations. International Journal of Manpower, 42(1), 193-209, https://doi.org/10.1108/IJM-09-2019-0448.
35. Mandal, S., Paul, J., Kotni, V.V.D.P., Chintaluri, M.G. (2024). The orientation of Gen Zs towards metaverse tourism. Journal of Destination Marketing & Management, 32, 100871, https://doi.org/10.1016/j.jdmm.2024.100871.
36. Mandal, S., Singh, R., Kotni, V.V.D.P., Chintaluri, M.G., Raman, R. (2025). Gen Zs and service robots: examination of contingency effects. International Journal of Quality and Service Sciences, 17(1), 67-89, https://doi.org/10.1108/IJQSS-04-2024-0041.
37. Medina-Molina, C., Pérez-Macías, N., Rey-Tienda, S., López-Sanz, M.E. (2025). Do ethical issues influence the interest of young people in using artificial intelligence? An integrated application of qualitative comparative analysis. Sustainable Technology and Entrepreneurship, 4(3), 100108, https://doi.org/10.1016/j.stae.2025.100108.
38. Merdin-Uygur, E., Ozturkcan, S. (2023). Consumers and service robots: Power relationships amid COVID-19 pandemic. Journal of Retailing and Consumer Services, 70, 103174, https://doi.org/10.1016/j.jretconser.2022.103174.
39. Milanesi, M., Monteverde, G., Runfola, A., Kursan Milaković, I., Guercini, S. (2024). Generation Z’s intention to use digital fashion items in the Metaverse. Journal of Consumer Marketing, 41(6), 674-689, https://doi.org/10.1108/JCM-11-2023-6405.
40. Minsky, M. (1968). Preface. In: M. Minsky (ed.). Semantic Information Processing. Cambridge, MA: MIT Press.
41. Misra, K., Singh, V., Zhang, Q. (2022). Frontiers: impact of stay-at-home-orders and cost-of-living on stimulus response: Evidence from the CARES Act. Marketing Science, 41(2), 211-229, https://doi.org/10.1287/mksc.2021.1329.
42. Olawumi, T.O., Chan, D.W.M. (2018). A Scientometric Review of Global Research on Sustainability and Sustainable Development. Journal of Cleaner Production, 183, 231-250, https://doi.org/10.1016/j.jclepro.2018.02.162.
43. Pandey, N., Gupta, N., Rastogi, S., Singh, R.R., Mishra, M. (2024). Digital coupons and Gen Z: An application of technology acceptance model with coupon proneness as a moderator. Innovative Marketing, 20(2), 65-77, https://doi.org/10.21511/im.20(2).2024.06.
44. Perianes-Rodriguez, A., Waltman, L., van Eck, N.J. (2016). Constructing bibliometric networks: a comparison between full and fractional counting. Journal of Informetrics, 10(4), 1178-1195, https://doi.org/10.1016/j.joi.2016.10.006.
45. Priporas, C-V., Stylos, N., Fotiadis, A.K. (2017). Generation Z consumers’ expectations of interactions in smart retailing: A future agenda. Computers in Human Behavior, 77, 374-381, https://doi.org/10.1016/j.chb.2017.01.058.
46. Romero, J., Lado, N. (2021). Service robots and COVID-19: exploring perceptions of prevention efficacy at hotels in generation Z. International Journal of Contemporary Hospitality Management, 33(11), 4057-4078, https://doi.org/10.1108/IJCHM-10-2020-1214.
47. Schewe, C.D., Meredith, G. (2004). Segmenting global markets by generational cohorts: determining motivations by age. Journal of Consumer Behaviour, 4(1), 51-63, https://doi.org/10.1002/cb.157.
48. Seyfi, S., Vo-Thanh, T., Zaman, M. (2024). Hospitality in the age of Gen Z: a critical reflection on evolving customer and workforce expectations. International Journal of Contemporary Hospitality Management, 36(13), 118-134, https://doi.org/10.1108/IJCHM-01-2024-003.
49. Shah, S.H.H., Lei, S., Ali, M., Doronin, D., Hussain, S.T. (2020). Prosumption: bibliometric analysis using HistCite and VOSviewer. Kybernetes, 49(3), 1020-1045, https://doi.org/10.1108/K-12-2018-0696.
50. Sowmya, G., Chakraborty, D., Polisetty, A., Khorana, S., Buhalis, D. (2023). Use of metaverse in socializing: application of the big five personality traits framework. Psychology & Marketing, 40(10), 2132-2150, https://doi.org/10.1002/mar.21863.
51. Srivastava, S., Mohta, A., Shunmugasundaram, V. (2024). Adoption of digital payment FinTech service by Gen Y and Gen Z users: evidence from India. Digital Policy, Regulation and Governance, 26(1), 95-117, https://doi.org/10.1108/DPRG-07-2023-0110.
52. Statista (2023). Daily phone screen time in the United States in 2023 by generation. Retrieved from https://www.statista.com/statistics/1178640/daily-phone-screen-time-by-gen-us/#:~:text=According%20to%20a%202023%20survey,three%20hours%20and%2030%20minute (4.04.2025).
53. The Alan Turing Institute (2025). What is artificial intelligence? Retrieved from https://www.turing.ac.uk/about-us/frequently-asked-questions (4.04.2025).
54. Van den Bergh, J., Pallini, K. (2018). Marketing to generation Z. Research World, 70, 18-23, https://doi.org/10.1002/rwm3.20660.
55. Vitezić, V., Perić, M. (2021). Artificial intelligence acceptance in services: connecting with Generation Z. The Service Industries Journal, 41(13-14), 926-946, https://doi.org/10.1080/02642069.2021.1974406.
56. Zhang, J., Quoquab, F., Mohammad, J. (2024). Metaverse tourism and Gen-Z and Gen-Y’s motivation: „will you, or won’t you travel virtually?”. Tourism Review, 79(2), 304-320, https://doi.org/10.1108/TR-06-2023-0393.
57. Zhong, B., Wu, H., Li, H., Sepasgozar, S., Hanbin Luo, H., He, L. (2019). A scientometric analysis and critical review of construction related ontology. Automation in Construction, 101, 17-31, https://doi.org/10.1016/j.autcon.2018.12.013.

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-db9c64a7-763e-4a45-9aa0-7ac1912ee282