This study integrates critical AI scholarship with relational communication theories to explain how AI language modifications shape the quality of government–citizen communication. Distinguishing between informational-cognitive quality (clarity, ease of response) and expressive-constitutive quality (politeness, respectfulness, feeling heard, trust, urgency, empathy), we hypothesize that AI yields uncontested benefits for the former but contested effects for the latter, potentially enhancing relational markers while muting authentic emotional cues. Using a vignette-based survey with 220 citizens and 214 civil servants in China, we assess perceptions across five interaction contexts: service requests, policy inquiries, complaints, suggestions, and emergencies. Results from paired t-tests and mixed-effects regressions support the claim that AI enhances both informational-cognitive and expressive-constitutive quality from the perspectives of citizens and civil servants, with significant improvements in clarity, politeness, satisfaction, trust, and empathy, but provide no consistent evidence of urgency or empathy signals. These findings suggest that concerns over algorithmic emotional flattening may be overstated or context-specific; they offer theoretical insights into AI-mediated public interactions and practical implications for fostering trust and efficiency in digital governance.
AichholzerG.StraußS. (2016). Collaborative forms of citizen (e-)participation. In & L. TorresG. Aichholzer, (Eds.), Evaluating e-participation (pp. 109–122). Springer International Publishing. https://doi.org/10.1007/978-3-319-25403-6_6
2.
AmosunT. S.ChuJ.RufaiO. H.MuhideenS.ShahaniR.GonlepaM. K. (2022). Does e-government help shape citizens’ engagement during the COVID-19 crisis? A study of mediational effects of how citizens perceive the government. Online Information Review, 46(5), 846–866. https://doi.org/10.1108/OIR-10-2020-0478
3.
AndroutsopoulouA.KaracapilidisN.LoukisE.CharalabidisY. (2019). Transforming the communication between citizens and government through AI-guided chatbots. Government Information Quarterly, 36(2), 358–367. https://doi.org/10.1016/j.giq.2018.10.001
4.
AnsellC.GashA. (2008). Collaborative governance in theory and practice. Journal of Public Administration Research and Theory, 18(4), 543–571. https://doi.org/10.1093/jopart/mum032
5.
BatsonC. D.EarlyS.SalvaraniG. (1997). Perspective taking: Imagining how another feels versus imaging how you would feel. Personality and Social Psychology Bulletin, 23(7), 751–758. https://doi.org/10.1177/0146167297237008
BaxterL. A.BraithwaiteD. O. (2008). Relational dialectics theory. In Engaging theories in interpersonal communication: Multiple perspectives (pp. 349–361). Sage.
8.
BenjaminR. (2019). Captivating Technology : Race, Carceral Technoscience, and Liberatory Imagination in Everyday Life. Duke University Press.
9.
BertotJ. C.JaegerP. T.GrimesJ. M. (2010). Using ICTs to create a culture of transparency: E-government and social media as openness and anti-corruption tools for societies. Government Information Quarterly, 27(3), 264–271. https://doi.org/10.1016/j.giq.2010.03.001
10.
BhattacherjeeA. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921
11.
BokayevB.ZhanzhigitovaZ.SadykovaK.BalmanovaA. (2023). Multichannel communication in kazakhstani public service: Examining the role of digital tools. Public Policy and Administration, 22(4), 535–547. https://doi.org/10.13165/VPA-23-22-4-11
12.
BrainardL. A.McNuttJ. G. (2010). Virtual government–citizen relations: Informational, transactional, or collaborative?Administration & Society, 42(7), 836–858. https://doi.org/10.1177/0095399710386308
13.
BurdenB. C.CanonD. T.MayerK. R.MoynihanD. P. (2012). The effect of administrative burden on bureaucratic perception of policies: Evidence from election administration. Public Administration Review, 72(5), 741–751. https://doi.org/10.1111/j.1540-6210.2012.02600.x
14.
BurgoonJ. K. (1993). Interpersonal expectations, expectancy violations, and emotional communication. Journal of Language and Social Psychology, 12(1–2), 30–48. https://doi.org/10.1177/0261927×93121003
15.
BurgoonJ. K.HaleJ. L. (1988). Nonverbal expectancy violations: Model elaboration and application to immediacy behaviors. Communication Monographs, 55(1), 58–79. https://doi.org/10.1080/03637758809376158
16.
CampionA.Gasco-HernandezM.Jankin MikhaylovS.EsteveM. (2022). Overcoming the challenges of collaboratively adopting artificial intelligence in the public sector. Social Science Computer Review, 40(2), 462–477. https://doi.org/10.1177/0894439320979953
17.
CharnessG.GneezyU.KuhnM. A. (2012). Experimental methods: Between-subject and within-subject design. Journal of Economic Behavior & Organization, 81(1), 1–8. https://doi.org/10.1016/j.jebo.2011.08.009
18.
ChenJ.PanJ.XuY. (2016). Sources of authoritarian responsiveness: A field experiment in China. American Journal of Political Science, 60(2), 383–400. https://doi.org/10.1111/ajps.12207
19.
Cortés-CedielM. E.Segura-TinocoA.CantadorI.Rodríguez BolívarM. P. (2023). Trends and challenges of e-government chatbots: Advances in exploring open government data and citizen participation content. Government Information Quarterly, 40(4), 101877. https://doi.org/10.1016/j.giq.2023.101877
20.
CrawfordK. (2021). The Atlas of AI : Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press.
21.
DavisM. H. (1983). Measuring individual differences in empathy: Evidence for a multidimensional approach. Journal of Personality and Social Psychology, 44(1), 113–126. https://doi.org/10.1037/0022-3514.44.1.113
22.
DavisonA. C.HinkleyD. V. (1997). Bootstrap Methods and Their Application. Cambridge University Press.
23.
DistelhorstG.HouY. (2017). Constituency service under nondemocratic rule: Evidence from China. The Journal of Politics, 79(3), 1024–1040. https://doi.org/10.1086/690948
24.
Di VaioA.HassanR.AlavoineC. (2022). Data intelligence and analytics: A bibliometric analysis of human–artificial intelligence in public sector decision-making effectiveness. Technological Forecasting and Social Change, 174, 121201. https://doi.org/10.1016/j.techfore.2021.121201
25.
DunleavyP.MargettsH.BastowS.TinklerJ. (2006). New public management is dead—long live digital-era governance. Journal of Public Administration Research and Theory, 16(3), 467–494. https://doi.org/10.1093/jopart/mui057
EdmondsonA. (1999). Psychological safety and learning behavior in work teams. Administrative Science Quarterly, 44(2), 350–383. https://doi.org/10.2307/2666999
28.
FossheimK.Lund-TønnesenJ. (2024). Digitalization of public sector organizations over time: The applicability of quantitative text analysis. International Review of Administrative Sciences, 90(2), 318–335. https://doi.org/10.1177/00208523231183569
29.
GalinskyA. D.MoskowitzG. B. (2000). Perspective-taking: Decreasing stereotype expression, stereotype accessibility, and in-group favoritism. Journal of Personality and Social Psychology, 78(4), 708–724. https://doi.org/10.1037/0022-3514.78.4.708
30.
GelmanA.HillJ. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.
HaesevoetsT.VerschuereB.Van SeverenR.RoetsA. (2024). How do citizens perceive the use of artificial intelligence in public sector decisions?Government Information Quarterly, 41(1), 101906. https://doi.org/10.1016/j.giq.2023.101906
HancockJ. T.NaamanM.LevyK. (2020). AI-mediated communication: Definition, research agenda, and ethical considerations. Journal of Computer-Mediated Communication, 25(1), 89–100. https://doi.org/10.1093/jcmc/zmz022
35.
HelbigN.Gil-GarcíaJ. R.FerroE. (2009). Understanding the complexity of electronic government: Implications from the digital divide literature. Government Information Quarterly, 26(1), 89–97. Scopus. https://doi.org/10.1016/j.giq.2008.05.004
36.
HoA. T. (2002). Reinventing local governments and the e-government initiative. Public Administration Review, 62(4), 434–444. https://doi.org/10.1111/0033-3352.00197
37.
HohensteinJ.KizilcecR. F.DiFranzoD.AghajariZ.MieczkowskiH.LevyK.NaamanM.HancockJ.JungM. F. (2023). Artificial intelligence in communication impacts language and social relationships. Scientific Reports, 13(1), 5487. https://doi.org/10.1038/s41598-023-30938-9
38.
HoxJ.MoerbeekM.SchootR. v. d. (2017). Multilevel Analysis: Techniques and Applications (Third Edition (3rd ed.)). Routledge. https://doi.org/10.4324/9781315650982
39.
JedličkováA. (2024). Ethical approaches in designing autonomous and intelligent systems: A comprehensive survey towards responsible development. AI and Society, 40(4), 2703–2716. https://doi.org/10.1007/s00146-024-02040-9
40.
JuJ.MengQ.SunF.LiuL.SinghS. (2023). Citizen preferences and government chatbot social characteristics: Evidence from a discrete choice experiment. Government Information Quarterly, 40(3), 101785. https://doi.org/10.1016/j.giq.2022.101785
41.
KadomaK.Aubin Le QuereM.FuX. J.MunschC.MetaxaD.NaamanM. (2024). The role of inclusion, control, and ownership in workplace AI-mediated communication. Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems, 1016, 1–10. https://doi.org/10.1145/3613904.3642650
42.
LarsenA. G.FølstadA. (2024). The impact of chatbots on public service provision: A qualitative interview study with citizens and public service providers. Government Information Quarterly, 41(2), 101927. https://doi.org/10.1016/j.giq.2024.101927
LiX.WangJ. (2024). Should government chatbots behave like civil servants? The effect of chatbot identity characteristics on citizen experience. Government Information Quarterly, 41(3), 101957. https://doi.org/10.1016/j.giq.2024.101957
45.
LindgrenI.MadsenCØHofmannS.MelinU. (2019). Close encounters of the digital kind: A research agenda for the digitalization of public services. Government Information Quarterly, 36(3), 427–436. https://doi.org/10.1016/j.giq.2019.03.002
46.
LiuY.MittalA.YangD.BruckmanA. (2022). Will AI console me when I lose my pet? Understanding perceptions of AI-mediated email writing. Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, 474, 1–13. https://doi.org/10.1145/3491102.3517731
47.
Lund-TønnesenJ. (2025). Privacy regimes, crisis strategies, and governments’ legitimizing of digital surveillance technology: Comparing Germany, Norway, and the United Kingdom. International Journal of Public Administration, 48(5–6), 381–394. https://doi.org/10.1080/01900692.2024.2344105
48.
MahmoodM.WeerakkodyV.ChenW. (2020). The role of information and communications technology in the transformation of government and citizen trust. International Review of Administrative Sciences, 86(4), 708–728. https://doi.org/10.1177/0020852318816798
49.
McNealR.HaleK.DotterweichL. (2008). Citizen–government interaction and the internet: Expectations and accomplishments in contact, quality, and trust. Journal of Information Technology & Politics, 5(2), 213–229. https://doi.org/10.1080/19331680802298298
50.
MergelI. (2013). A framework for interpreting social media interactions in the public sector. Government Information Quarterly, 30(4), 327–334. https://doi.org/10.1016/j.giq.2013.05.015
51.
MoynihanD.HerdP.HarveyH. (2015). Administrative burden: Learning, psychological, and compliance costs in citizen-state interactions. Journal of Public Administration Research and Theory, 25(1), 43–69. https://doi.org/10.1093/jopart/muu009
52.
MussagulovaA. (2026). How is empathy used in public service? A systematic literature review. Public Management Review, 28(2), 334–358. https://doi.org/10.1080/14719037.2024.2438891
53.
NielsenJ. A.ElmholdtK. T.NoesgaardM. S. (2024). Leading digital transformation: A narrative perspective. Public Administration Review, 84(4), 589–603. https://doi.org/10.1111/puar.13721
54.
NobleS. U. (2018). Algorithms of Oppression: How Search Engines Reinforce Racism. New York University Press.
55.
OliverR. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17(4), 460–469. https://doi.org/10.1177/002224378001700405
PietersonW. J.EbbersW. E. (2020). Channel choice evolution: An empirical analysis of shifting channel behavior across demographics and tasks. Government Information Quarterly, 37(3), 101478. https://doi.org/10.1016/j.giq.2020.101478
59.
PislaruM.VladC. S.IvascuL.MirceaI. I. (2024). Citizen-centric governance: Enhancing citizen engagement through artificial intelligence tools. Sustainability, 16(7), 2686. https://doi.org/10.3390/su16072686
60.
PorumbescuG. A. (2016). Linking public sector social media and e-government website use to trust in government. Government Information Quarterly, 33(2), 291–304. https://doi.org/10.1016/j.giq.2016.04.006
61.
PurcellZ. A.DongM.NussbergerA.KöbisN.JakeschM. (2024). People have different expectations for their own versus others’ use of AI-mediated communication tools. British Journal of Psychology, 0(0), 1–19. https://doi.org/10.1111/bjop.12727
62.
RussellS. J.NorvigP. (2010). Artificial intelligence: A modern approach (3rd ed.). Pearson.
63.
SivarajahU.IraniZ.WeerakkodyV. (2015). Evaluating the use and impact of web 2.0 technologies in local government. Government Information Quarterly, 32(4), 473–487. https://doi.org/10.1016/j.giq.2015.06.004
64.
SousaW. G. D.MeloE. R. P. D.BermejoP. H. D. S.FariasR. A. S.GomesA. O. (2019). How and where is artificial intelligence in the public sector going? A literature review and research agenda. Government Information Quarterly, 36(4), 101392. https://doi.org/10.1016/j.giq.2019.07.004
65.
StoneJ. A.FlandersK. J.CanS. H. (2022). Strategic communication? Measurement and evaluation of twitter use among municipal governments. Government Information Quarterly, 39(4), 101755. https://doi.org/10.1016/j.giq.2022.101755
SwellerJ.Van MerrienboerJ. J. G.PaasF. G. W. C. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10(3), 251–296. https://doi.org/10.1023/a:1022193728205
68.
SzydlowskiG.De BoerN.TummersL. (2022). Compassion, bureaucrat bashing, and public administration. Public Administration Review, 82(4), 619–633. https://doi.org/10.1111/puar.13485
69.
Tejedo-RomeroF.AraujoJ. F. F. E.TejadaÁRamírezY. (2022). E-government mechanisms to enhance the participation of citizens and society: Exploratory analysis through the dimension of municipalities. Technology in Society, 70, 101978. https://doi.org/10.1016/j.techsoc.2022.101978
70.
ThomasJ. C.StreibG. (2003). The new face of government: Citizen-initiated contacts in the era of e-government. Journal of Public Administration Research and Theory, 13(1), 83–102. https://doi.org/10.1093/jopart/mug010
71.
TorresL.PinaV.AcereteB. (2005). E-government developments on delivering public services among EU cities. Government Information Quarterly, 22(2), 217–238. https://doi.org/10.1016/j.giq.2005.02.004
72.
Van NoordtC.MisuracaG. (2022). Artificial intelligence for the public sector: Results of landscaping the use of AI in government across the European union. Government Information Quarterly, 39(3), 101714. https://doi.org/10.1016/j.giq.2022.101714
73.
VialG. (2019). Understanding digital transformation: A review and a research agenda. Journal of Strategic Information Systems, 28(2), 118–144. https://doi.org/10.1016/j.jsis.2019.01.003
74.
WaltherJ. B. (1992). Interpersonal effects in computer-mediated interaction: A relational perspective. Communication Research, 19(1), 52–90. https://doi.org/10.1177/009365092019001003
75.
WaltherJ. B. (1996). Computer-mediated communication: Impersonal, interpersonal, and hyperpersonal interaction. Communication Research, 23(1), 3–43. https://doi.org/10.1177/009365096023001001
WangH.TangZ. R.XiongL.WangX.ZhuL. (2025). What determinants influence citizens’ engagement with mobile government social media during emergencies? A net valence model. Government Information Quarterly, 42(1), 101995. https://doi.org/10.1016/j.giq.2024.101995
78.
WeiJ.WangX.SchuurmansD.BosmaM.IchterB.XiaF.ChiE.LeQ. V.ZhouD. (2022). Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35, 24824–24837. https://dl.acm.org/doi/10.5555/3600270.3602070
79.
WelchE. W.HinnantC. C.MoonM. J. (2005). Linking citizen satisfaction with e-government and trust in government. Journal of Public Administration Research and Theory, 15(3), 371–391. https://doi.org/10.1093/jopart/mui021
80.
WirtzB. W.WeyererJ. C.GeyerC. (2019). Artificial intelligence and the public sector—applications and challenges. International Journal of Public Administration, 42(7), 596–615. https://doi.org/10.1080/01900692.2018.1498103
81.
XiaoY.YuS. (2025). Can ChatGPT replace humans in crisis communication? The effects of AI-mediated crisis communication on stakeholder satisfaction and responsibility attribution. International Journal of Information Management, 80, 102835. https://doi.org/10.1016/j.ijinfomgt.2024.102835
YigitcanlarT.DavidA.LiW.FookesC.BibriS. E.YeX. (2024). Unlocking artificial intelligence adoption in local governments: Best practice lessons from real-world implementations. Smart Cities, 7(4), 1576–1625. https://doi.org/10.3390/smartcities7040064
