Abstract
The current advancement of information technologies has created the conditions to introduce and popularize e-government, bringing citizens closer to public administration. Yet, e-government faces challenges such as the digital divide, civic data overload, lack of trust in government institutions and their online services. Artificial intelligence has the potential to address many of those challenges but also raises ethical, privacy, and security concerns. Which requires that before successfully adopting such a disruptive technology, it is imperative to delve into the drivers leading to citizens’ acceptance first. Consequently, this study proposes an empirical model to explore and better understand the citizens’ acceptance towards the use of AI in e-government. We used an online survey to collect data (N = 208). The results reveal that the perceived usefulness and trust of AI and social influence significantly contribute to the acceptance of AI use in e-government. Despite the majority being aware of AI and e-government, some are not or are not aware of how AI can be used in e-government. The findings of this study can help local and national governments assess the acceptance of the adoption of AI-based technologies in e-government and define tailored strategies to respond to citizens’ concerns and highlight benefits to society.
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References
Stone, P., et al.: One hundred year study on artificial intelligence (2015)
Twentyman, J.: Intelligent economies: AI’s transformation of industries and societies (2018)
Council of Europe: Ad hoc Committee on Artificial Intelligence (CAHAI) Feasibility Study (2020)
Berryhill, J., Heang, K.K., Clogher, R., McBride, K.: Hello, world: artificial intelligence and its use in the public sector. OECD Observatory of Public Sector Innovation (OPSI)., pp.1–148 (2019). https://doi.org/10.1787/726fd39d-en
EY, Microsoft: Artificial Intelligence in the Public Sector: European Outlook for 2020 and Beyond. Microsoft (2020)
Savaget, P., Chiarini, T., Evans, S.: Empowering political participation through artificial intelligence. Sci. Public Policy 46, 369–380 (2019). https://doi.org/10.1093/scipol/scy064
König, P.D., Wenzelburger, G.: Opportunity for renewal or disruptive force? How artificial intelligence alters democratic politics. Govern. Inf. Q. 37 (2020). https://doi.org/10.1016/j.giq.2020.101489
Fast, E., Horvitz, E.: Long-term trends in the public perception of artificial intelligence. In: 31st AAAI Conference on Artificial Intelligence, AAAI 2017, pp. 963–969 (2017)
OECD: The Case for E-Government: Excerpts from the OECD Report The E-Government Imperative. OECD J. Budget. 3, 1987–1996 (2003)
Mishra, S.S., Geleta, A.T.: Can an E-government system ensure citizens’ satisfaction without service delivery? Int. J. Public Adm. 43, 242–252 (2020). https://doi.org/10.1080/01900692.2019.1628053
le Blanc, D.: E-participation: a quick overview of recent qualitative trends. DESA Working Paper. Though the goal of realising citizen centricity ha (2020)
Chen, K., Aitamurto, T.: Barriers for crowd’s impact in crowdsourced policymaking: civic data overload and filter hierarchy. Int. Public Manag. J. 22, 99–126 (2019). https://doi.org/10.1080/10967494.2018.1488780
Toots, M.: Why E-participation systems fail: the case of Estonia’s Osale.ee. Govern. Inf. Q. 36, 546–559 (2019). https://doi.org/10.1016/j.giq.2019.02.002
Arana-Catania, M., et al.: Citizen participation and machine learning for a better democracy. Digit. Govern.: Res. Pract. 2, 1–22 (2021). https://doi.org/10.1145/3452118
Al-Mushayt, O.S.: Automating E-government services with artificial intelligence. IEEE Access 7, 146821–146829 (2019). https://doi.org/10.1109/ACCESS.2019.2946204
Zuiderwijk, A., Chen, Y.C., Salem, F.: Implications of the use of artificial intelligence in public governance: a systematic literature review and a research agenda. Govern. Inf. Q. 101577 (2021). https://doi.org/10.1016/j.giq.2021.101577
van Ittersum, K., Rogers, W., Capar, M.: Understanding technology acceptance: phase 1–literature review and qualitative model development. Technology Report …. 0170, 1–123 (2006)
Belanche, D., Casaló, L.V., Flavián, C.: Artificial Intelligence in FinTech: understanding robo-advisors adoption among customers. Ind. Manage. Data Syst. 119, 1411–1430 (2019). https://doi.org/10.1108/IMDS-08-2018-0368
Davis, F.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13, 319–340 (1989)
Albarrán Lozano, I., Molina, J.M., Gijón, C.: Perception of artificial intelligence in Spain. Telematics Inform. 63, 101672 (2021). https://doi.org/10.1016/j.tele.2021.101672
Nadarzynski, T., Miles, O., Cowie, A., Ridge, D.: Acceptability of artificial intelligence (AI)-led chatbot services in healthcare: a mixed-methods study. Digit. Health 5 (2019). https://doi.org/10.1177/2055207619871808
Kelley, P.G., et al.: Exciting, useful, worrying, futuristic: public perception of artificial intelligence in 8 countries. In: AIES 2021 - Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, pp. 627–637. Virtual Event (2021). https://doi.org/10.1145/3461702.3462605
Cho, S.H., Oh, S.Y., Rou, H.G., Gim, G.Y.: A study on the factors affecting the continuous use of e-government services - focused on privacy and security concerns -. In: Proceedings - 20th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2019, pp. 351–361. IEEE (2019). https://doi.org/10.1109/SNPD.2019.8935693
Araujo, T., et al.: In AI we trust? Perceptions about automated decision-making by artificial intelligence. 35, 611–623 (2020). https://doi.org/10.1007/s00146-019-00931-w
Gesk, T.S., Leyer, M.: Artificial intelligence in public services: when and why citizens accept its usage. Gov. Inf. Q. 39, 101704 (2022). https://doi.org/10.1016/j.giq.2022.101704
Lichtenthaler, U.: Extremes of acceptance: employee attitudes toward artificial intelligence. J. Bus. Strateg. 41, 39–45 (2019). https://doi.org/10.1108/JBS-12-2018-0204
Bitkina, O.V., Jeong, H., Lee, B.C., Park, J., Park, J., Kim, H.K.: Perceived trust in artificial intelligence technologies: a preliminary study. Hum. Factors Ergon. Manuf. 30, 282–290 (2020). https://doi.org/10.1002/hfm.20839
Taylor, S., Todd, P.A.: Understanding information technology usage: a test of competing models. Inf. Syst. Res. 6, 144–176 (1995). https://doi.org/10.1287/isre.6.2.144
Chen, Y.N.K., Wen, C.H.R.: Impacts of attitudes toward government and corporations on public trust in artificial intelligence. Commun. Stud. 72, 115–131 (2021). https://doi.org/10.1080/10510974.2020.1807380
Starke, C., Marcinkowski, F., Wintterlin, F.: Social networking sites, personalization, and trust in government: empirical evidence for a mediation model (2020). https://doi.org/10.1177/2056305120913885
Gefen, D.: E-commerce: the role of familiarity and trust. Omega (Westport) 28, 725–737 (2000). https://doi.org/10.1016/S0305-0483(00)00021-9
Baek, T., Morimoto, M.: Stay away from me. J. Advert. 41, 59–76 (2012). https://doi.org/10.2753/JOA0091-3367410105
Boerman, S.C., Kruikemeier, S., Zuiderveen Borgesius, F.J.: Exploring motivations for online privacy protection behavior: insights from panel data. Communic Res. 48, 953–977 (2021). https://doi.org/10.1177/0093650218800915
LaRose, R., Rifon, N.J.: Promoting i-safety: effects of privacy warnings and privacy seals on risk assessment and online privacy behavior. J. Consum. Aff. 41, 127–149 (2007). https://doi.org/10.1111/j.1745-6606.2006.00071.x
Bhattacherjee, A.: Acceptance of e-commerce services: the case of electronic brokerages. IEEE Trans. Syst. Man Cybern. - Part A: Syst. Hum. 30, 411–420 (2000). https://doi.org/10.1109/3468.852435
Pechar, E., Bernauer, T., Mayer, F.: Beyond political ideology: the impact of attitudes towards government and corporations on trust in science. Sci. Commun. 40, 291–313 (2018). https://doi.org/10.1177/1075547018763970
Marcinkowski, F., Starke, C.: Trust in government: what’s news media got to do with it? Stud. Commun. Sci. 18, 87–102 (2018). https://doi.org/10.24434/j.scoms.2018.01.006
Starke, C., Lünich, M.: Artificial intelligence for political decision-making in the European union: effects on citizens’ perceptions of input, throughput, and output legitimacy. Data Policy 2 (2020). https://doi.org/10.1017/dap.2020.19
Hair, J.F., Ringle, C.M., Sarstedt, M.: Partial least squares structural equation modeling: rigorous applications, better results and higher acceptance. Long Range Plann. 46, 1–12 (2013). https://doi.org/10.1016/j.lrp.2013.01.001
Jöreskog, K.G.: Simultaneous factor analysis in several populations. Psychometrika 36, 409–426 (1971). https://doi.org/10.1007/BF02291366
Castañeda, J.A., Muñoz-Leiva, F., Luque, T.: Web Acceptance model (WAM): moderating effects of user experience. Inf. Manage. 44, 384–396 (2007). https://doi.org/10.1016/j.im.2007.02.003
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Moreira, J., Naranjo-Zolotov, M. (2024). Exploring Potential Drivers of Citizen’s Acceptance of Artificial Intelligence Use in e-Government. In: Rocha, A., Adeli, H., Dzemyda, G., Moreira, F., Colla, V. (eds) Information Systems and Technologies. WorldCIST 2023. Lecture Notes in Networks and Systems, vol 801. Springer, Cham. https://doi.org/10.1007/978-3-031-45648-0_33
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