The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
AI-based self-service technology in public service delivery: User experience and influencing factors
Abstract
Abstract Public sectors are utilizing AI-based self-service technology (SST) at an accelerating rate, given its potential for improving work efficiency and user experience, reducing service costs, and relieving human workloads. However, there is a limited understanding of the factors influencing citizens' user experience when services supported by AI-based SST are provided. Thus, with insights from the Consumer Value Theory, this paper aims to explore the factors that are important to AI-based SST user experience and the conditional role of trust in government. The on-site survey of 379 citizens in a public service center in China indicates that user experience positively relates to personalization and aesthetics and negatively associates with perceived time spent on the AI-based self-service machines. In addition, the results suggest that citizens with more trust in government are more likely to have a pleasant experience coming from AI-based SST's personalization and aesthetics. Public managers should ensure that the AI-based SST is aesthetically appealing and should be able to personalize the delivery of the right contents to the right person at the right time. Furthermore, they should always prioritize cultivating more trust from citizens to achieve a more positive user experience.
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Cites
The future of employment: How susceptible are jobs to computerisation?
Frey, C. B., & Osborne, M. A. (2017) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2016.08.019 · Cited by 8,727
Customer experience creation: Determinants, dynamics and management strategies
Verhoef, P. C., Lemon, K. N., Parasuraman, A., Roggeveen, A., Tsiros, M., & Schlesinger, L. A. (2009) · Journal of Retailing · doi:10.1016/j.jretai.2008.11.001 · Cited by 3,140
What can machine learning do? Workforce implications
Brynjolfsson, E., & Mitchell, T. (2017) · Science · doi:10.1126/science.aap8062 · Cited by 1,131
Flow online: lessons learned and future prospects.
Hoffman, D. L., & Novak, T. P. (2009) · Journal of Interactive Marketing · doi:10.1016/j.intmar.2008.10.003 · Cited by 954
Sun, T. Q., & Medaglia, R. (2019) · Government Information Quarterly · doi:10.1016/j.giq.2018.09.008 · Cited by 948
Dubey, R., Gunasekaran, A., Childe, S. J., Bryde, D. J., Giannakis, M., Foropon, C., Roubaud, D., & Hazen, B. T. (2020) · International Journal of Production Economics · doi:10.1016/j.ijpe.2019.107599 · Cited by 889
Transforming the communication between citizens and government through AI-guided chatbots
Androutsopoulou, A., Karacapilidis, N., Loukis, E., & Charalabidis, Y. (2019) · Government Information Quarterly · doi:10.1016/j.giq.2018.10.001 · Cited by 567
Omigie, N. O., Zo, H., Rho, J. J., & Ciganek, A. P. (2017) · Industrial Management & Data Systems · doi:10.1108/imds-06-2016-0228 · Cited by 85
Cited by
Laux, J., Wachter, S., & Mittelstadt, B. (2024) · Regulation & Governance · doi:10.1111/rego.12512 · Cited by 365
Artificial intelligence in public services: When and why citizens accept its usage
Gesk, T. S., & Leyer, M. (2022) · Government Information Quarterly · doi:10.1016/j.giq.2022.101704 · Cited by 195
Yigitcanlar, T., Li, R. Y. M., Beeramoole, P. B., & Paz, A. (2023) · Government Information Quarterly · doi:10.1016/j.giq.2023.101833 · Cited by 142
Wang, C., Li, Y., Fu, W., & Jin, J. (2023) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2023.103325 · Cited by 126
Anshari, M., Hamdan, M., Ahmad, N., & Ali, E. (2025) · Journal of Science and Technology Policy Management · doi:10.1108/jstpm-07-2023-0123 · Cited by 44
Metadata
- Authors
- Chen, T., Guo, W., Gao, X., & Liang, Z.
- Venue
- Government Information Quarterly
- Publisher
- Elsevier BV
- Published
- 2021-10
- Volume
- 38
- Issue
- 4
- Pages
- 101520
- ISSN
- 0740-624X (ISSN-L)
- OpenAlex
- W3080671792
- Cited by
- 268
- References
- 9
- FWCI
- 72.30
- Type
- journal article
- Language
- en
- Open access
- closed
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