References
- Algieri, B., Aquino, A., & Succurro, M. (2013). Technology transfer offices and academic spin-off creation: the case of Italy. Journal of Technology Transfer, 38(4), 382–400. https://doi.org/10.1007/s10961-011-9241-8
- Audretsch, D. B. (2014). From the entrepreneurial university to the university for the entrepreneurial society. Journal of Technology Transfer, 39(3), 313–321. https://doi.org/10.1007/s10961-012-9288-1
- AUTM. (2021–2024). AUTM U.S. Licensing Activity Survey (FY2021-FY2023) – definitions and instructions (incl. “startup company”). Association of University Technology Managers. https://autm.net
- Benneworth, P., & Charles, D. (2005). University spin-off policies and economic development in less successful regions. European Planning Studies, 13(4), 537–557. https://doi.org/10.1080/09654310500107175
- Berman, E. P. (2008). Why did universities start patenting? Institution-building and the road to the Bayh–Dole Act. Social Studies of Science, 38(6), 835–871. https://doi.org/10.1177/0306312708098605
- Bigliardi, B., Galati, F., & Verbano, C. (2013). Evaluating performance of university spin-off companies: Lessons from Italy. Journal of Technology Management & Innovation, 8(2), 178–188. https://doi.org/10.4067/S0718-27242013000200015
- Bray, M. J., & Lee, J. N. (2000). University revenues from technology transfer: Licensing fees vs. equity positionsshares. Journal of Business Venturing, 15(5-6), 385–402. https://doi.org/10.1016/S0883-9026(98) 00034-2
- Caputo, A., Charles, D., & Fiorentino, R. (2022). University spin-offs: entrepreneurship, growth and regional development. Studies in Higher Education, 47(10), 1999–2006. https://doi.org/10.1080/03075079. 2022.2122655
- Cerver Romero, E., Ferreira, J. J., & Fernandes, C. I. (2021). The multiple faces of the entrepreneurial university: A review of the prevailing theoretical approaches. Journal of Technology Transfer, 46(4), 1173-1195. https://doi.org/10.1007/s10961-020-09809-7
- Clarysse, B., & Moray, N. (2004). A process study of entrepreneurial team formation: The case of a research-based spin-off. Journal of Business Venturing, 19(1), 55–79. https://doi.org/10.1016/S0883-9026(02)00113-1
- Clarysse, B., Tartari, V., & Salter, A. (2011). The impact of entrepreneurial capacity, experience and organizational support on academic entrepreneurship. Research Policy, 40(8), 1084–1093. https://doi.org/10.1016/j.respol.2011.05.010
- Coates Ulrichsen, T., Roupakia, Z., & Kelleher, L. (2022). Busting myths and moving forward: the reality of UK university approaches to taking equity in spinouts. Policy Evidence Unit for University Commercialisation technical report. University of Cambridge. https://doi.org/10.17863/CAM.118883
- Council of the European Union. (2022). Council Recommendation on the guiding principles for knowledge valorisation (OJ C 493, 9.12.2022, pp. 1–12). EUR-Lex. https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:32022H2415
- Dabić, M., Vlačić, B., Guerrero, M., & Daim, T. U. (2022). University spin-offs: the past, the present, and the future. Studies in Higher Education, 47(10), 2007–2021. https://doi.org/10.1080/03075079.2022.2122656
- Ensley, M. D., & Hmieleski, K. M. (2005). A comparative study of new venture top management team composition, dynamics and performance between university-based and independent start-ups. Research Policy, 34(7), 1091–1105. https://doi.org/10.1016/j.respol.2005.05.008
- Etzkowitz, H., & Leydesdorff, L. (1997). Universities and the global knowledge economy: A triple helix of university-industry relations. Cassell.
- European Commission: Directorate-General for Research and Innovation. (2023). Code of practice on standardisation in the European Research Area: Commission recommendation. Publications Office of the European Union. https://data.europa.eu/doi/10.2777/371128
- Gilson, R. J., & Schizer, D. M. (2003). Understanding Venture Capital Structure: A Tax Explanation for Convertible Preferred Stock. Harvard Law Review, 116(3), 874–916. https://doi.org/10.2307/1342584
- Goethner, M., Obschonka, M., Silbereisen, R. K., & Cantner, U. (2012). Scientists’ transition to academic entrepreneurship: Economic and psychological determinants. Journal of economic psychology, 33(3), 628–641. https://doi.org/10.1016/j.joep.2011.12.002
- Guerrero, M., Fayolle, A., Di Guardo, M. C., & Urbano, D. (2024). Re-viewing the entrepreneurial university: strategic challenges and theory building opportunities. Small Business Economics, 63, 527–548. https://doi.org/10.1007/s11187-023-00858-z
- Heirman, A., & Clarysse, B. (2004). How and why do research-based start-ups differ at founding? A resource-based configurational perspective. Journal of Technology Transfer, 29(3-4), 247–268. https://doi.org/10.1023/B:JOTT.0000034122.88495.0d
- Kaplan, S. N., & Strömberg, P. (2003). Financial contracting theory meets the real world: An empirical analysis of venture capital contracts. Review of Economic Studies, 70(2), 281–315. https://doi.org/10.1111/1467-937X.00245
- Konopka-Cupiał, G. (2020). Centra transferu technologii i spółki celowe jako narzędzia komercjalizacji wyników badań naukowych w polskich uczelniach [Technology transfer centres and special purpose vehicles as tools for commercialisation of scientific research at Polish universities]. Studia BAS, 1(60), 75–86. https://doi.org/10.31268/StudiaBAS.2020.05 (in Polish)
- Landes, D. S. (1969, 2003). The unbound Prometheus: Technological change and industrial development in Western Europe from 1750 to the present. Cambridge University Press.
- Lockett, A., & Wright, M. (2005). Resources, capabilities, risk capital and the creation of university spin-out companies. Research Policy, 34(7), 1043–1057. https://doi.org/10.1016/j.respol.2005.05.006
- McAdam, M., & McAdam, R. (2008). High tech start-ups in University Science Park incubators: The relationship between the start-up’s lifecycle progression and use of the incubator’s resources. Technovation, 28(5), 277–290. https://doi.org/10.1016/j.technovation.2007.07.012
- Mian, S. A. (1997). Assessing and managing the university technology business incubator: An integrative framework. Journal of Business Venturing, 12(4), 251–285. https://doi.org/10.1016/S0883-9026(96)00063-8
- Miranda, F. J., Chamorro, A., & Rubio, S. (2018). Re-thinking university spin-off: A critical literature review and a research agenda. Journal of Technology Transfer, 43(4), 1007–1038. https://doi.org/10.1007/s10961-017-9647-z
- Mokyr, J. (2002). The Gifts of Athena: Historical Origins of the Knowledge Economy. Princeton University Press. https://doi.org/10.1515/9781400829439
- Mowery, D. C. (2005). The Bayh–Dole act and high-technology entrepreneurship in US Universities: Chicken, egg, or something else? In: Gary D. Libecap (Ed). University Entrepreneurship and Technology Transfer (pp. 39–68). Emerald. https://doi.org/10.1016/S1048-4736(05)16002-0
- Mustar, P., Renault, M., Colombo, M. G., Piva, E., Fontes, M., Lockett, A., … & Moray, N. (2006). Conceptualising the heterogeneity of research-based spin-offs: A multi-dimensional taxonomy. Research Policy, 35(2), 289–308. https://doi.org/10.1016/j.respol.2005.11.001
- Nerkar, A., & Shane, S. (2003). When do start-ups that exploit patented academic knowledge survive?. International Journal of Industrial Organization, 21(9), 1391–1410. https://doi.org/10.1016/S0167-7187(03) 00088-2
- Nicolaou, N., & Birley, S. (2003). Academic networks in a trichotomous categorisation of university spinouts. Journal of Business Venturing, 18(3), 333–359. https://doi.org/10.1016/S0883-9026(02)00118-0
- O’Reilly, N. M., Robbins, P., & Scanlan, J. (2018). Dynamic capabilities and the entrepreneurial university: a perspective on the knowledge transfer capabilities of universities. Journal of Small Business & Entrepreneurship, 31(3), 243–263. https://doi.org/10.1080/08276331.2018.1490510
- O’Shea, R. P., Chugh, H., & Allen, T. J. (2008). Determinants and consequences of university spinoff activity: A conceptual framework. Journal of Technology Transfer, 33(6), 653–666. https://doi.org/10.1007/s10961-007-9060-0
- OECD. (2003). OECD Science, Technology and Industry Scoreboard 2003. OECD Publishing. https://doi.org/10.1787/sti_scoreboard-2003-en
- OECD/Eurostat. (2018). Oslo Manual 2018: Guidelines for Collecting, Reporting and Using Data on Innovation (4th ed.). OECD Publishing. https://doi.org/10.1787/9789264304604-en
- Ortín-Ángel, P., & Vendrell-Herrero, F. (2014). University spin-offs vs. other NTBFs: Total factor productivity differences at outset and evolution. Technovation, 34(2), 101–112. https://doi.org/10.1016/j.technovation.2013.09.006
- Perkmann, M., Salandra, R., Tartari, V., McKelvey, M., & Hughes, A. (2021). Academic engagement: A review of the literature 2011–2019. Research Policy, 50(1), 104114. https://doi.org/10.1016/j.respol.2020.104114
- Perkmann, M., Tartari, V., McKelvey, M., Autio, E., Broström, A., D’Este, P., … & Sobrero, M. (2013). Academic engagement and commercialisation: A review of the literature on university-industry relations. Research Policy, 42(2), 423–442. https://doi.org/10.1016/j.respol.2012.09.007
- Pinheiro, M. L., Pinho, J. C., & Lucas, C. (2015). The outset of UI R & D relationships: the specific case of biological sciences. European Journal of Innovation Management, 18(3), 282–306. https://doi.org/10.1108/EJIM-08-2014-0085
- Pirnay, F., Surlemont, B., & Nlemvo, F. (2003). Toward a typology of university spin-offs. Small Business Economics, 21(4), 355–369. https://doi.org/10.1023/A:1026167105153
- Polish Law on Higher Education and Science. (2018, July 20). Prawo o szkolnictwie wyższym i nauce. (2018, July 20). Dziennik Ustaw, 2018, item 1668 (consolidated text for 2025) https://isap.sejm.gov.pl/isap.nsf/download.xsp/WDU20180001668/U/D20181668Lj.pdf (in Polish)
- Rappert, B., Webster, A., & Charles, D. (1999). Making sense of diversity and reluctance: academic-industrial relations and intellectual property. Research Policy, 28(8), 873–890. https://doi.org/10.1016/S0048-7333(99)00028-1
- Rodríguez-Gulías, M. J., Rodeiro-Pazos, D., & Fernández-López, S. (2016). The Regional Effect on the Innovative Performance of University Spin-Offs: a Multilevel Approach. Journal of Knowledge Economy, 7(4), 869–889. https://doi.org/10.1007/s13132-015-0287-y
- Shane, S. (2004). Academic entrepreneurship: University spinoffs and wealth creation. Edward Elgar Publishing. https://doi.org/10.4337/9781843769828
- Smith, A. (1776). An inquiry into the nature and causes of the wealth of nations: Volume One. Printed for W. Strahan; and T. Cadell.
- Soete, L., & Freeman, C. (1997). The Economics of Industrial Innovation (1st ed.). Routledge. https://doi.org/10.4324/9780203357637
- Stevens, A. J. (2004). The enactment of Bayh–Dole. The Journal of Technology Transfer, 29(1), 93–99. https://doi.org/10.1023/B:JOTT.0000011183.40867.52
- Tracey, I., & Williamson, A. (2023). Independent review of university spin-out companies: Final report and recommendations. UK Department for Science, Innovation & Technology. https://www.gov.uk/government/publications/independent-review-of-university-spin-out-companies
- van Eck, N. J., & 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
- Walter, A., Auer, M., & Ritter, T. (2006). The impact of network capabilities and entrepreneurial orientation on university spin-off performance. Journal of Business Venturing, 21(4), 541–567. https://doi.org/10.1016/j.jbusvent.2005.02.005
- Atsmon, Y., Baroudy, K., Jain, P., Kishore, S., McCarthy, B., Nair, S., & Saleh, T. (2021). Tipping the scales in AI: How leaders capture exponential returns. McKinsey & Company Report.
- Barnett, T., Pearson, A. W., Pearson, R., & Kellermanns, F. W. (2015). Five-factor model personality traits as predictors of perceived and actual usage of technology. European Journal of Information Systems, 24(4), 374–390.
- Bedué, P., & Fritzsche, A. (2022). Can we trust AI? An empirical investigation of trust requirements and guide to successful AI adoption. Journal of Enterprise Information Management, 35(2), 530–549.
- Blut, M., & Wang, C. (2020). Technology readiness: A meta-analysis of conceptualizations of the construct and its impact on technology use. Journal of the Academy of Marketing Science, 48(4), 649–669.
- Booyse, D., & Scheepers, C. B. (2024). Barriers to adopting automated organisational decision-making through the use of artificial intelligence. Management Research Review, 47(1), 64–85.
- Chugh, R., Turnbull, D., Morshed, A., Sabrina, F., Azad, S., Md Mamunur, R., & Subramani, S. (2025). The promise and pitfalls: A literature review of generative artificial intelligence as a learning assistant in ICT education. Computer Applications in Engineering Education, 33(2), e70002.
- Daly, S. J., Wiewiora, A., & Hearn, G. (2025). Shifting attitudes and trust in AI: Influences on organizational AI adoption. Technological Forecasting and Social Change, 215, 124108.
-
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003.
Davis F. D. Bagozzi R. P. Warshaw P. R. ( 1989 ). User acceptance of computer technology: A comparison of two theoretical models . Management Science , 35 ( 8 ), 982 – 1003 .
- Dhagarra, D., Goswami, M., & Kumar, G. (2020). Impact of trust and privacy concerns on technology acceptance in healthcare: An Indian perspective. International Journal of Medical Informatics, 141, 104164.
- Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., … Williams, M. D. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 102–147.
- Feuerriegel, S., Hartmann, J., Janiesch, C., Zschech, P., Heinzl, A., & Hund, A. (2024). Generative AI. Business & Information Systems Engineering, 66(2), 111–126.
- Fuglsang, S. (2024). What if some people just do not like science? How personality traits relate to attitudes toward science and technology. Public Understanding of Science, 33(5), 623–633.
- Gamma, F., & Magistretti, S. (2025). Artificial intelligence in innovation management: A review of innovation capabilities and a taxonomy of AI applications. Journal of Product Innovation Management, 42(1), 76–111.
- Gramlich, J. (2025). Q&A: Why and how we compared the public’s views of artificial intelligence with those of AI experts. Pew Research Center.
- Grassini, S., & Koivisto, M. (2024). Understanding how personality traits, experiences, and attitudes shape negative bias toward AI-generated artworks. Scientific Reports, 14(1), 4113.
- Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage.
- Hornung, O., & Smolnik, S. (2021). AI invading the workplace: Negative emotions towards the organizational use of personal virtual assistants. Electronic Markets, 32(1), 123–138.
- Hubert, M., Blut, M., Brock, V., Zhang, R. W., Koch, V., & Riedl, R. (2019). The influence of acceptance and adoption drivers on smart home usage. European Journal of Marketing, 53(6), 1073–1098.
- IBM Institute for Business Value. (2024). The ingenuity of generative AI: Unlock productivity and innovation at scale. IBM.
- Jha, K., Doshi, A., Patel, P., & Shah, M. (2019). A comprehensive review on automation in agriculture using artificial intelligence. Artificial Intelligence in Agriculture, 2, 1–12.
- Johnson, R. A., & Wichern, D. W. (1992). Applied multivariate statistical analysis. Prentice Hall.
- Kaya, F., Aydin, F., Schepman, A., Rodway, P., Yetişensoy, O., & Demir Kaya, M. (2024). The roles of personality traits, AI anxiety, and demographic factors in attitudes toward artificial intelligence. International Journal of Human–Computer Interaction, 40(2), 497–514.
- Kassa, B. Y., & Worku, E. K. (2025). The impact of artificial intelligence on organizational performance: The mediating role of employee productivity. Journal of Open Innovation: Technology, Market, and Complexity, 11, 100474.
- Keeter, S. (2019). Growing and improving Pew Research Center’s American Trends Panel. Pew Research Center.
- Kelly, J. (2023). Goldman Sachs predicts 300 million jobs will be lost or degraded by artificial intelligence. Forbes.
- Kim, B. J., Kim, M. J., & Lee, J. (2025). The dark side of artificial intelligence adoption: Linking artificial intelligence adoption to employee depression via psychological safety and ethical leadership. Humanities and Social Sciences Communications, 12, 704.
- Liu, Y., Sheng, F., & Liu, R. (2025). Generative AI adoption and employee outcomes: A conservation of resources perspective on job crafting, career commitment, and the moderating role of liking of AI. Humanities and Social Sciences Communications, 12, 1376.
- Mariani, M., & Dwivedi, Y. K. (2024). Generative artificial intelligence in innovation management: A preview of future research developments. Journal of Business Research, 175, 114542.
- Mariani, M. M., Perez-Vega, R., & Wirtz, J. (2022). AI in marketing, consumer research and psychology: A systematic literature review and research agenda. Psychology and Marketing, 39(4), 755–776.
- Meuter, M. L., Ostrom, A. L., Bitner, M. J., & Roundtree, R. (2003). The influence of technology anxiety on consumer use experiences with self-service technologies. Journal of Business Research, 56(11), 899–906.
- Montag, C., Ali, R., & Davis, K. L. (2025). Affective neuroscience theory and attitudes towards artificial intelligence. AI & Society, 40(1), 167–174.
- Montag, C., & Ali, R. (2025). Can we assess attitudes toward AI with single items? Associations with existing attitudes toward AI measures and trust in ChatGPT. Journal of Technology in Behavioral Science, 1–11.
- Monteverde, G., Cammarota, A., Serafini, L., & Quadri, M. (2025). Are we human or are we voice assistants? Revealing the interplay between anthropomorphism and consumer concerns. Journal of Marketing Management, 41(1–2), 1–25.
- Mousavizadeh, M., Kim, D. J., & Chen, R. (2016). Effects of assurance mechanisms and consumer concerns on online purchase decisions: An empirical study. Decision Support Systems, 92, 79–90.
- Morsi, S. (2023). Artificial intelligence in electronic commerce: Investigating the customers’ acceptance of using chatbots. Electronic Commerce Research, 13(3), 156–176.
- Organization for Economic Cooperation and Development (OECD). (2019). OECD AI principles overview. OECD.
- Ozsevim, I. (2023). Consumer concerns: AI privacy, transparency and emotionality. AI Magazine.
- Pandy, G., Pugazhenthi, V. J., & Murugan, A. (2025). Generative AI: Transforming the landscape of creativity and automation. International Journal of Computer Applications, 186(63), 7–13.
- Parasuraman, A., & Colby, C. L. (2015). An updated and streamlined technology readiness index: TRI 2.0. Journal of Service Research, 18(1), 59–74.
- Park, S. S., Tung, C. D., & Lee, H. (2021). The adoption of AI service robots: A comparison between credence and experience service settings. Psychology & Marketing, 38(4), 691–703.
- Park, J., & Woo, S. E. (2022). Who likes artificial intelligence? Personality predictors of attitudes toward artificial intelligence. Journal of Psychology, 156(1), 68–94.
- Păvăloaia, V.-D., & Necula, S.-C. (2023). Artificial intelligence as a disruptive technology – A systematic literature review. Electronics, 12(5), 1102.
- Pew Research Center. (2021). American Trends Panel wave 99 [Data files and questionnaire].
- Qualtrics. (2023). Beyond chatbots, majority of consumers are open to AI in legal, medical or financial matters. Qualtrics News.
- Querci, I., Barbarossa, C., Romani, S., & Ricotta, F. (2022). Explaining how algorithms work reduces consumers’ concerns regarding the collection of personal data and promotes AI technology adoption. Psychology & Marketing, 39(10), 1888–1901.
- Rahimi, B., Nadri, H., Afshar, H. L., & Timpka, T. (2018). A systematic review of the technology acceptance model in health informatics. Applied Clinical Informatics, 9(3), 604–634.
- Rainie, L., Anderson, J., & Vogels, E. A. (2021). Experts doubt ethical AI design will be broadly adopted as the norm within the next decade. Pew Research Center.
- Rainie, L., Funk, C., Anderson, M., & Tyson, A. (2022). AI and human enhancement: Americans’ openness is tempered by a range of concerns. Pew Research Center.
- Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210.
- Rana, N. P., Pillai, R., Sivathanu, B., & Malik, N. (2024). Assessing the nexus of generative AI adoption, ethical considerations and organizational performance. Technovation, 135, 103064.
- Rashidi, H. H., Pantanowitz, J., Hanna, M. G., Tafti, A. P., Sanghani, P., Buchinsky, A., & Pantanowitz, L. (2025). Introduction to artificial intelligence and machine learning in pathology and medicine: Generative and nongenerative artificial intelligence basics. Modern Pathology, 38(4), 100688.
- Reddy, P., Ch, K., Sharma, K., Sharma, B., & Sharma, S. (2025). Evolution of generative artificial intelligence: A review of the developed and developing. Engineered Science, 35, 1529.
- Romeo, E., & Lacko, J. (2025). Adoption and integration of AI in organizations: A systematic review of challenges and drivers towards future directions of research. Kybernetes, Advance online publication.
- Shell, M. A., & Buell, R. W. (2022). Mitigating the negative effects of consumer anxiety through access to human contact (Harvard Business School Working Paper No. 19-089). Harvard Business School.
- Schiavo, G., Businaro, S., & Zancanaro, M. (2024). Comprehension, apprehension, and acceptance: Understanding the influence of literacy and anxiety on acceptance of artificial intelligence. Technology in Society, 77, 102537.
- Sidoti, O., Park, E., & Gottfried, J. (2025). About a quarter of U.S. teens have used ChatGPT for schoolwork – double the share in 2023. Pew Research Center.
- Siegrist, M., & Hartmann, C. (2020). Consumer acceptance of novel food technologies. Nature Food, 1(6), 343–350.
- Skoumpopoulou, D., Wong, A., Ng, P., & Lo, M. F. (2018). Factors that affect the acceptance of new technologies in the workplace: A cross case analysis between two universities. International Journal of Education and Development Using Information and Communication Technology, 14(3), 209–222.
- Smith, G. K. (2025). Strategic integration of generative AI: Opportunities, challenges, and organizational impacts. Law, Economics and Society, 1(1), 156–179.
- Special Committee on Artificial Intelligence in a Digital Age (AIDA). (2022). Report on artificial intelligence in a digital age. European Parliament.
- Stein, J. P., Messingschlager, T., Gnambs, T., Hutmacher, F., & Appel, M. (2024). Attitudes towards AI: Measurement and associations with personality. Scientific Reports, 14(1), 2909.
- Stokel-Walker, C., & Van Noorden, R. (2023). What ChatGPT and generative AI mean for science. Nature, 614(7947), 214–216.
- Tamilmani, K., Rana, N. P., Fosso Wamba, S., & Dwivedi, R. (2021). The extended unified theory of acceptance and use of technology (UTAUT2): A systematic literature review and theory evaluation. International Journal of Information Management, 57, 102269.
- United States Census Bureau. (2023). 2023 population QuickFacts.
- Wang, C., Li, X., Liang, Z., Sheng, Y., Zhao, Q., & Chen, S. (2025). The roles of social perception and AI anxiety in individuals’ attitudes toward ChatGPT in education. International Journal of Human–Computer Interaction, 41(9), 5713–5730.
- Wang, G., Obrenovic, B., Gu, X., & Godinic, D. (2025). Fear of the new technology: Investigating the factors that influence individual attitudes toward generative Artificial Intelligence (AI). Current Psychology, 44, 8050–8067.
- White House. (2022). The impact of artificial intelligence on the future of work forces in the European Union and the United States of America.
- Wilson, H. J., & Daugherty, P. R. (2018). Collaborative intelligence: Humans and AI are joining forces. Harvard Business Review.
- Wixom, B. H., & Todd, P. A. (2005). A theoretical integration of user satisfaction and technology acceptance. Information Systems Research, 16(1), 85–102.
- Youn, S., & Lee, K.-H. (2019). Proposing value based technology acceptance model: Testing on paid mobile media service. Fashion and Textiles, 6(13), 1–16.
- Yuan, C., Zhang, C., & Wang, S. (2022). Social anxiety as a moderator in consumer willingness to accept AI assistants based on utilitarian and hedonic values. Journal of Retailing and Consumer Services, 68, 103101.