Awareness and readiness to use artificial intelligence by the adult population of Ukraine: Survey results
-
Author(s)Svitlana TarasenkoLink to ORCID Index: https://orcid.org/0000-0002-4829-0559
,
Oleksandra KarintsevaLink to ORCID Index: https://orcid.org/0000-0001-9570-3646
,
Wojciech DuranowskiLink to ORCID Index: https://orcid.org/0000-0002-3984-6332
,
Artem BilovolLink to ORCID Index: https://orcid.org/0009-0000-5823-1379
,
Viacheslav VoronenkoLink to ORCID Index: https://orcid.org/0000-0002-0301-5924
-
DOIhttp://dx.doi.org/10.21511/ppm.22(4).2024.01
-
Article InfoVolume 22 2024, Issue #4, pp. 1-13
- 171 Views
-
31 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
Policymakers, educators, and businesses must develop artificial intelligence-related initiatives and strategies to effectively engage and benefit the population. The study aims to evaluate awareness and readiness to utilize artificial intelligence by the adult population of Ukraine in 2022. The paper employed a questionnaire consisting of two sets of questions: 1) awareness of artificial intelligence and 2) readiness to use artificial intelligence. A total of 806 respondents were interviewed via an online survey. The margin of error does not exceed 5%. The results indicate that while Ukrainians have a generally positive view of artificial intelligence, they remain skeptical about the prospect of robots functioning as workplace partners. The majority find it difficult to envision collaborating with a robot in a professional setting (only 36.9% of Ukrainians are ready to work with a robot). The survey highlights that the primary benefits of AI products and services valued by Ukrainians include timesaving, increased comfort, and enhanced service accessibility. Ukrainian men demonstrate a greater degree of commitment and awareness of artificial intelligence products/services than Ukrainian women. Young people are the most informed age group about artificial intelligence products/services. Residents of the western regions indicate a more significant impact of artificial intelligence on the present, unlike respondents from the eastern regions of Ukraine.
Acknowledgment
This research was funded by a grant “Restructuring of the national economy in the direction of digital transformations for sustainable development” (№0122U001232) from National Research Foundation.
- Keywords
-
JEL Classification (Paper profile tab)O21, Z13, Z18
-
References35
-
Tables10
-
Figures5
-
- Figure 1. Are you ready to cooperate with a robot/artificial intelligence? %
- Figure 2. In your opinion, which of the following products/services use AI? %
- Figure 3. What AI products/services do you use in your daily life/business? %
- Figure 4. What AI products/services would you integrate into your daily life?
- Figure 5. Would you like to learn about available technologies or opportunities to apply AI? %
-
- Table 1. Select the statements about artificial intelligence you agree with (by gender), %
- Table 2. Select the statements about artificial intelligence you agree with (by cluster), %
- Table 3. In your opinion, which of the following products/services use AI (by gender)
- Table 4. In your opinion, which of the following products/services use AI? (by age), %
- Table 5. Are you ready to cooperate with a robot/artificial intelligence? (by gender), %
- Table 6. Are you ready to cooperate with a robot/artificial intelligence? (by age), %
- Table 7. What AI products/services do you use in your daily life/business? (by gender), %
- Table 8. What AI products/services do you use in your daily life/business? (by age), %
- Table 9. Would you like to receive additional information on available technologies and opportunities for AI use? (by age), %
- Table 10. Would you like to receive additional information on available technologies and opportunities for AI use? (by cluster), %
-
- Amann, J., Vayena, E., Ormond, K. E., Frey, D., Madai, V. I., & Blasimme, A. (2023). Expectations and attitudes towards medical artificial intelligence: A qualitative study in the field of stroke. PLoS ONE, 18(1), Article e0279088.
- Androshchuk, G. O. (2021). Stupin doviry do shtuchnoho intelektu: Analiz rezultativ doslidzhen [Degree of trust in artificial intelligence: Analysis of research results]. Chasopys Kyivskoho Universytetu Prava, 3. 200-206. (In Ukrainian).
- Ariani, A. F., Napitupulu, D., Jati, R. K., Kadar, J. A., & Syafrullah, M. (2018). Testing of technology readiness index model based on exploratory factor analysis approach. Journal of Physics: Conference Series, 1007, Article 012043.
- Avcı, Y. U., & Gulbahar, Y. (2013). Technology acceptance model: A review of the prior predictors. Ankara University Journal of Faculty of Educational Sciences (JFES), 46(1), 89-109.
- Beauchene, V., Bellefonds, N., Duranton, S., & Mills, S. (2023, June 7). AI at work: What people are saying. BCG.
- Brauner, P., Hick, A., Philipsen, R., & Ziefle, M. (2023). What does the public think about artificial intelligence? – A criticality map to understand bias in the public perception of AI. Frontiers in Computer Sciences, 5, Article 1113903.
- Cave, S., & ÓhÉigeartaigh, S. S. (2018). Bridging near- and long-term concerns about AI. Nature Machine Intelligence, 1(1), 5-6.
- Cave, S., Craig, C., Dihal, K., Dillon, S., Montgomery, J., Singler, B., & Taylor, L. (2018). Portrayals and perceptions of AI and why they matter. The Royal Society.
- Corea, F., Fossa, F., Loreggia, A., Quintarelli, S., & Sapienza, S. (2022). A principle-based approach to AI: The case for European Union and Italy. AI & SOCIETY, 38, 521-535.
- Criado, J. I., & Gil-Garcia, R. J. (2019). Creating public value through smart technologies and strategies: From digital services to artificial intelligence and beyond. International Journal of Public Sector Management, 32(5), 438-450.
- Dafoe, A. (2018). AI governance: A research agenda. University of Oxford.
- Davis, F. D. (1989). Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Quarterly, 13(3), 318-340.
- Dieter, D. G., & Gessler, E. C. (2021). A preferred reality: Film portrayals of robots and AI in n popular science fiction. Journal of Science & Popular Culture, 4(1), 59-76.
- Flavián, C., Pérez-Rueda, A., Belanche, D., & Casaló, L.V. (2022). Intention to use analytical artificial intelligence (AI) in services – The effect of technology readiness and awareness. Journal of Service Management, 33(2), 293-320.
- Hadlington, L., Karanika-Murray, M., Slater, J., Binder, J., Gardner, S., & Knight, S. (2024). Public perceptions of the use of artificial intelligence in defence: A qualitative exploration. AI & Society.
- Hong, V., & Lim, J. (2022). Effects of country and individual factors on public acceptance of artificial intelligence and robotics technologies: A multilevel SEM analysis of 28-country survey data. Behaviour & Information Technology, 41(7), 1515-1528.
- Hradecky, D., Kennell, J., Cai, W., & Davidson, R. (2022). Оrganizational readiness to adopt artificial intelligence in the exhibition sector in Western Europe. International Journal of Information Management, 65, Article 102497.
- Ipsos. (2023). Global views on A.I. 2023. How people across the world feel about artificial intelligence and expect it to impact their lives. A 31-country Global Advisor survey.
- Krarup, T., & Horst, M. (2023). European artificial intelligence policy as digital single market making. Big Data & Society, 10(1).
- Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40(3), 191-204.
- Parasuraman, A., & Colby, C. L. (2015). An updated and streamlined technology readiness index: TRI 2.0. Journal of Service Research, 18(1), 59-74.
- Robles, Р., & Mallinson, D. J. (2023). Artificial intelligence technology, public trust, and effective governance. Review of Policy Research.
- Rojahn, J., Palu, A., Skiena, S., & Jones, J. J. (2023). American public opinion on artificial intelligence in healthcare. PLoS ONE, 18(11), Article e0294028.
- Sage, K., Kaye, Sh-A., & Oviedo-Trespalacios, O. (2023). What factors contribute to the acceptance of artificial intelligence? A systematic review. Telematics and Informatics, 77, Article 101925.
- Samaieva, Yu. (2023, July 10). Stavlennia ukraintsiv do shtuchnoho intelektu na dyvo lehkovazhne. Darma [The attitude of Ukrainians to artificial intelligence is surprisingly frivolous. They should not]. Mirror of the Week. (In Ukrainian).
- Schepman, A., & Rodway, P. (2022). The general attitudes towards artificial intelligence scale (GAAIS): confirmatory validation and associations with personality, corporate distrust, and general trust. International Journal of Human-Computer Interaction, 39(13), 2724-2741.
- Schiff, K. J., Schiff, D. S., Adams, I. T., McCrain, J., & Mourtgos, S. M. (2023). Institutional factors driving citizen perceptions of AI in government: Evidence from a survey experiment on policing. Public Administration Review.
- Tallberg, J., Erman, E., Furendal, M., Geith, J., Klamberg, M., & Lundgren, M. (2023). The global governance of artificial intelligence: Next steps for empirical and normative research. International Studies Review, 25(3), Article viad040.
- Talley, S. M. (2020). Public acceptance of AI technology in self-flying aircraft. Journal of Aviation/Aerospace Education & Research, 29(1).
- UNCTAD. (2021). Technology and innovation report. Catching technological waves. Innovation with equity (196 p.). Geneva: United Nations.
- Venkatesh, V., Morris, M., Davis, G., & Davis, F. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.
- Wang, P. (2019). On defining artificial intelligence. Journal of Artificial General Intelligence, 10(2), 1-37.
- Wirtz, B. W., Weyerer, J. C., & Kehl, I. (2022). Governance of artificial intelligence: A risk and guideline-based integrative framework. Government Information Quarterly, 39(4), Article 101685.
- Yeh, S., Wu, A., Yu, H., Wu, H. C., Kuo, Y., & Chen, P. (2021). Public perception of artificial intelligence and its connections to the sustainable development goals. Sustainability, 13(16), Article 9165.
- Zhang, B., & Dafoe, A. (2020). U.S. Public opinion on the governance of artificial intelligence. AIES 2020 – Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (pp. 187-193). New York, USA.
-
Fintech in the eyes of Millennials and Generation Z (the financial behavior and Fintech perception)
Mohannad A. M. Abu Daqar ,
Samer Arqawi ,
Sharif Abu Karsh
doi: http://dx.doi.org/10.21511/bbs.15(3).2020.03
Banks and Bank Systems Volume 15, 2020 Issue #3 pp. 20-28 Views: 6187 Downloads: 2123 TO CITE АНОТАЦІЯThis study investigates the Millennials and Gen Z perception toward Fintech services, their usage intention, and their financial behavior. The study took place in the Palestinian context with a global comparison among these generations. The authors used the questionnaire-based technique to meet the study objective. West Bank respondents were selected for this purpose; the study instrument was distributed through different social media channels. The findings show that reliability/trust and ease of use are the main issues in using a financial service. Millennials are more aware (48%) of Fintech services than Gen Z (38%), which is different from the global view where Gen Z is the highest. The smartphone penetration rate is 100% among both generations, while the financial inclusion ratio in Palestine is around 36.4%; these clear indicators are the main Fintech drivers to promote Fintech services in Palestine, and these are global indicators for Fintech adoption intention. Both generations (84%) intend to use e-wallet services, Millennials (87%) and Gen Z is (70%) prefer using real-time services. Half of the respondents see that Fintech plays a complementary role with banks. The majority see that Fintech services are cheaper than bank services. Wealth management, and robot advisor services, and both generations are looking to acquire them in the long run. The authors revealed that 85% of respondents from both generations trust banks, so it is recommended that banks digitize their financial services to meet the customers’ needs, considering that 90% of respondents see that promotions are a key issue in adopting Fintech services. Promoting e-wallet services by banks is highly recommended due to the massive rivalry with Fintech parties.
-
Adoption of Mobile Banking and Perceived Risk in GCC
Kavita Chavali ,
Ajith Kumar
doi: http://dx.doi.org/10.21511/bbs.13(1).2018.07
Banks and Bank Systems Volume 13, 2018 Issue #1 pp. 72-79 Views: 2456 Downloads: 569 TO CITE АНОТАЦІЯThe study deals with the adoption of mobile banking services by respondents in UAE and the perception of risk factors by them. A model was developed on the Customer Adoption Process of mobile banking. The model is validated based on the data collected using the questionnaire from a sample of 90 respondents in UAE. Factor analysis is used to evaluate and analyze the responses. Belief in technology and the value it creates are the major driving force for respondents to adopt mobile banking. Respondents perceive that mobile banking helps in proper financial planning due to continuous monitoring the transactions and time saving. Lack of privacy in the mobile banking transactions and not all banks offering mobile banking services in UAE are the major challenges perceived by the respondents for non-adoption of mobile banking. Respondents identify time risk, financial risk and performance risk as the most predominant risk factors compared to other risks in the adoption process.
-
Data science methods for comprehensive assessment of regional economic development
Liubov Chagovets ,
Svitlana Prokopovych ,
Viktor Kholod
doi: http://dx.doi.org/10.21511/dm.18(2).2020.05
The paper deals with the assessment of the socio-economic development of Ukrainian regions using Data Science methods and multidimensional analysis, including taxonomy, n-dimensional classification, and ensemble decision trees methods. The methodological bases of economic regions devel¬opment by the economic and mathematical modeling methods were investigated. The necessity of improving and further developing estimation models of the regional economic development using business analytics tools and multidimensional scaling methods was investigated.
The ensemble decision trees methods was applied for the classification model of economic development of the Ukrainian regions according to the conceptual base of the research on regional econom¬ic development. It will increase the quality level of administrative decisions making on regional de¬velopment asymmetry equalization. It is determined that in Ukraine, there is a significant imbalance of regions clusters with high and low economic level. Here was investigated the relationship between the two groups of economic development indicators – the indicators of the economic development regional performance and the group of economic potential. The results of the classification model allow identifying the set of indicators that have significant impact on the overall economic development. The developed ensemble model allows carrying out qualitative recognition and prediction of the state probability of economic development. It will improve the quality of decision making pro¬cesses on equalization of regional development asymmetry.
The further research gives the possibility to develop the system of levers directions of regional development imbalance equalization, to determine priority vectors of sustainable development of both the regions and the country.
