With the rapid development of artificial intelligence (AI), countries are increasingly adopting AI-guided chatbots to improve service on government portals. The reduction in face-to-face services under COVID-19 pandemic will further accelerate this trend. However, the adoption and performance of the existing chatbots differ. Based on the literature on e-government adoption and innovative policy innovation diffusion, this article examines both the initial and postadoption stages of chatbot usage in China’s local governments. While the first phase employs the survival model of event history analysis to explore the factors that influence the decision to adopt chatbots in local government, the second phase analyzes the determinants of those chatbots’ performance in the postadoption stage. We find that pressure factors and readiness factors play different roles in the different adoption stages. Although pressure can encourage local governments to implement chatbots, these governments’ readiness determined how well the chatbots perform after their initial adoption. The implications and limitations of the research are also discussed.
AllenM. D.PettusC.Haider-MarkelD. P. (2004). Making the national local: Specifying the conditions for national government influence on state policymaking. State Politics & Policy Quarterly, 4(3), 318–344.
2.
AndersenK. V.HenriksenH. Z. (2005). The first leg of e-government research: Domains and application areas 1998–2003. International Journal of Electronic Government Research, 1(4), 26–44.
3.
AndroutsopoulouA.KaracapilidisN.LoukisE.CharalabidisY. (2019). Transforming the communication between citizens and government through AI-guided chatbots. Government Information Quarterly, 36(2), 358–367.
4.
ArmstrongS.BostromN.SchulmanC. (2016). Racing to the precipice: A model of artificial intelligence development. AI & Society, 31, 201–206.
5.
BarthT. J.ArnoldE. (1999). Artificial intelligence and administrative discretion: Implications for public administration. The American Review of Public Administration, 29(4), 332–351.
6.
BaybeckB.BerryW. B.SiegelD. A. (2011). A strategic theory of policy diffusion via intergovernmental competition. The Journal of Politics, 73(1), 232–247.
7.
BerndW. W.WeyererJ. C.GeyerC. (2019). Artificial intelligence and the public sector-applications and challenges. International Journal of Public Administration, 42(7), 596–615.
8.
BerryF.BerryW. (1990). State lottery adoptions as policy innovations: An event history analysis. American Political Science Review, 84(2), 395–415.
9.
BerryF.BerryW. (1992). Tax innovation in the states: Capitalizing on political opportunity. American Journal of Political Science, 36(3), 715–742.
10.
BerryF.BerryW. (1999). Innovation and diffusion models in policy research. In SabatierP. (Ed.), Theories of the policy process (pp. 223–248). Westview.
11.
BoehmkeF. J.WitmerR. (2004). Disentangling diffusion: The effects of social learning and economic competition on state policy innovation and expansion. Political Research Quarterly, 57(1), 39–51.
12.
BousheyG. (2010). Policy diffusion dynamics in America. Cambridge University Press.
13.
BraunD.GilardiF. (2006). Taking “Galton’s problem” seriously towards a theory of policy diffusion. Journal of Theoretical Politics, 3, 298–322.
14.
BrownC. (1971). Empirical regularities in the diffusion of innovation. Annals of the Association of American Geographers, 3, 551–559.
15.
CaoX. (2012). Global networks and domestic policy convergence: A network explanation of policy changes. World Politics, 64(3), 375–425.
16.
ColbyK. (1975). Artificial paranoia: A computer simulation of paranoid processes. Elsevier.
17.
CovadongaM. (2005). Policy learning, policy diffusion, and the making of a new order. The Annals of the American Academy of Political and Social Science, 598(1), 67–82.
18.
CukierK. (2018). The data-driven world. In FranklinD. (Ed.), Megatech: Technology in 2050 (Chapter14, pp. 164–173). Profile Books.
19.
DamanpourF. (1991). Organizational innovation: A meta-analysis of effects of determinants and moderators. Academy of Management Journal, 34(3), 555–590.
20.
DavisF. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
DinuV. (2009). Dimensions of quality in services. Amfiteatru Economic, 26(1), 243–244.
23.
DwivediY. K.HughesL.IsmagilovaE.AartsG.CoombsC.CrickT.DuanY.DwivediR.EdwardsJ.EirugA.GalanosV.IlavarasanP. V.JanssenM.JonesP.KarA. K.KizginH.KronemannB.LalB.LuciniB.…WilliamsM. D. (2019). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management. Advance online publication. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
24.
EasingwoodC. J.LunnS. O. (1992). Diffusion paths in a high-tech environment: Clusters and commonalities. R&D Management, 1, 69–80.
25.
EggersW. D.SchatskyD.ViechnickP. (2017). AI-augmented government: Using cognitive technologies to redesign public sector work [M]. Deloitte University Press, 5, 1–23.
26.
EleanorD. G. (2001). Key factors influencing innovation in government. The Innovation Journal: The Public Sector Innovation Journal, 6(2), 1–20.
27.
FordH.DuboisE.PuschmannC. (2016). Keeping Ottawa honest—One Tweet at a time? Politicians, journalists, Wikipedians and their Twitter bots. Journal of Communication, 10(October), 4891–4914.
28.
FreemanC.PerezC. (1988). Structural crises of adjustment, business cycles and investment behaviour. In DosiG.FreemanC.NelsonR.SilverbergG.SoeteL. (Eds.), Technical change and economic theory (pp. 38–66). Pinter.
29.
GangW. Y.BoS.ChenS. M.YiZ. C.ZiM. P. (2014, August). Chinese intelligent chat robot based on the AIML language. In TangY. (Eds.), 2014 Sixth International Conference on Intelligent Human-Machine Systems and Cybernetics (Vol. 1, pp. 367–370). IEEE.
GrayV. (1973). Innovation in the states: A diffusion study. The American Political Science Review, 67(4), 1174–1185.
33.
GrayV. (1974). Expenditures and innovation as dimensions of progressivism: A note on the America states. American Journal of Political Science, 18(4), 693–699.
34.
GunawanA. A.MulyonoP. R.BudihartoW. (2018). Indonesian question answering system for solving arithmetic word problems on intelligent humanoid robot. Procedia Computer Science, 135, 719–726.
35.
GurkaynakG.YilmazI.HakseverG. (2016). Stifling artificial intelligence: Human perils. Computer Law & Security Review, 32, 749–758.
HeywoodS. J. (1965). Toward a sound theory of innovation. The Elementary School Journal, 66(3), 107–114.
38.
HollidayI.YepR. (2005). E-government in China. Public Administration and Development, 25(3), 239–249.
39.
JinH.LiangJ. (2015). How should the Chinese government provide information services for Mongol ethnic minority?Government Information Quarterly, 23(1), 82–94.
40.
KhayyamH.JavadiB.JaliliM.JazarR. N. (2020). Artificial intelligence and internet of things for autonomous vehicles. In JazarR.DaiL. (Eds.), Nonlinear approaches in engineering applications (pp. 39–68). Springer.
41.
KlievinkB.JanssenM. (2009). Realizing joined-up government—Dynamic capabilities and stage models. Government Information Quarterly, 26(2), 275–284.
42.
LayneK.LeeJ. (2001). Developing fully functional E-government: A four stage model. Government Information Quarterly, 18(2), 122–136.
43.
LewinK. (1951). Field theory in social science. Harper & Brother Publisher, p. 239.
44.
LiH.ZhouL. A. (2005). Political turnover and economic performance: The incentive role of personnel control in China. Journal of Public Economics, 89(9–10), 1743–1762.
45.
MaL. (2013). The diffusion of government microblogging: Evidence from Chinese municipal police Bureaus. Public Management Review, 15(2), 288–309.
46.
MakridakisS. (2017). The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures, 90, 46–60.
47.
McCarthyJ.MinskyM. L.RochesterN.ShannonC. E. (1955). A proposal for the Dartmouth summer research project on artificial intelligence. Artificial Intelligence: Critical Concepts, 2, 44–53.
48.
McCauleyL. (2007). AI Armageddon and the three laws of robotics. Ethics and Information Technology, 9(2), 153–164.
MenzelD. C.FellerI. (1977). Leadership and interaction patterns in the diffusion of innovations among the American states. The Western Political Quarterly, 30(4), 528–536.
51.
MikhaylovS. J.EsteveM.CampionA. (2018). Artificial intelligence for the public sector: Opportunities and challenges of cross-sector collaboration. Philosophical Transactions of the Royal Society A, 376, 20170357.
52.
MintromM. (1997). Policy entrepreneurs and the diffusion of innovation. American Journal of Political Science, 42(3), 738–770.
53.
MohrL. B. (1969). Determinants of innovation in organizations. The American Political Science Review, 63(1), 111–126.
54.
MoonM. J. (2002). The evolution of e-government amongst municipalities: Rhetoric or reality?Public Administration Review, 62(4), 424–433.
55.
NataleS.BallatoreA. (2017). Imagining the thinking machine: Technological myths and the rise of artificial intelligence. Convergence, 26, 3–18. https://doi.org/10.1177/1354856517715164
56.
NaudéW.DimitriN. (2009). The race for an artificial general intelligence: Implications for public policy. AI & Society, 35, 367–379. https://doi.org/10.1007/s00146-019-00887-x
57.
NogueraE.LlopisF.FerrandezA.EscapaA. (2007). Evaluation of open-domain question answering systems within a time constraint (Vol 1, pp. 260265). 21st International Conference on Advanced Information Networking and Applications Workshops (AINAW’07), Niagara Falls, Ontario, Canada.
58.
OhH. J.LeeC. H.HwangY. G.JangM. G.ParkJ. G.LeeY. K. (2007). A case study of edutainment robot: Applying voice question answering to intelligent robot (pp. 410–415). RO-MAN 2007 - The 16th IEEE International Symposium on Robot and Human Interactive Communication, Jeju Island, South Korea.
59.
PowellD. M. W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review, 48(2), 147–160.
60.
RogersE. M. (1983). Diffusion of innovations. Free Press.
61.
SabatierP. A. (2007). Theories of the policy process. Westview Press.
62.
SavageR. L. (1985). When a policy’s time has come: Cases of rapid policy diffusion 1983–1984. The Journal of Federalism, 15(3), 111–126.
63.
SchumpeterJ. A.BackhausU. (2003). The theory of economic development. In BackhausJ. (Ed.), Joseph Alois Schumpeter. The European heritage in economics and the social sciences (Vol. 1). Springer.
64.
ShawJ.RudziczF.JamiesonT.GoldfarbA. (2019). Artificial intelligence and the implementation challenge. Journal of Medical Internet Research, 21(7), e13659. https://doi.org/10.2196/13659.
65.
ShieberS. (1994). Lessons from a restricted Turing test. Communications of the Association for Computing Machinery, 37(6), 70–78.
66.
ShipanC. R.VoldenC. (2008). The mechanisms of policy diffusion. American Journal of Political Science, 52(4), 840–857.
67.
ShipanC. R.VoldenC. (2012). Policy diffusion: Seven lessons for scholars and practitioners. Public Administration Review, 72(6), 788–796.
68.
SiauK.LongY. (2005). Synthesizing e-government stage models: A meta-synthesis based on meta ethnographic approach. Industry Management & Data Systems, 150(4), 443–458.
69.
SirkemaaS. (2010). Towards integrated electronic services in public sector. In MastorakisN. E.MladenovV.BojkovicZ. S. (Eds.), International Conference on Computers - Proceedings (Vol. 1, pp. 766–770).
70.
SotalaK.YampolskiyR. V. (2013). Responses to catastrophic AGI risk: A survey (Technical Reports, 2013(2)). Machine Intelligence Research Institute.
StephenJ. K.RosenbergN. (1986). An overview of innovation in the positive sum strategy: Harnessing technology for economic growth [M]. National Academy Press, pp. 289–298.
74.
StrangD.MeyerJ. W. (1993). Institutional conditions for diffusion. Theory and Society, 22(4), 487–511.
75.
SunT. Q.MedagliaR. (2019). Mapping the challenges of Artificial Intelligence in the public sector: Evidence from public healthcare. Government Information Quarterly, 36(2), 368–383.
76.
SussmanS. W.SiegalW. S. (2003). Informational influence in organizations: An integrated approach to knowledge adoption. Information Systems Research, 14(1), 47–65.
77.
TolbertC. J.MossbergerK.McNealR. (2008). Institutions, policy innovation, and E-government in the American States. Public Administration Review, 68(3), 549–563.
78.
TollD.LindgrenI.MelinU.MadsenC. Ø. (2019). Artificial intelligence in Swedish policies: Values, benefits, considerations and risks. In LindgrenI., et al. (Eds.), Electronic Government. EGOV 2019: Lecture notes in computer science (Vol. 11685, pp. 301–310). Springer.
79.
TornatzkyL. G.FleischerM.CharkrabartiA. K. (1990). The processes of technological innovation [M]. Lexington Books, pp. 9–27.
80.
TurbanE.OutlandJ.KingD.LeeJ. K.LiangT. P.TurbanD. C. (2018). Innovative EC systems: From E-Government to E-Learning, E-Health, sharing economy, and P2P commerce. Electronic Commerce 2018. Springer Texts in Business and Economics, Springer, Cham.
81.
United Nations. (2003). UN Global E-government Survey 2003. Undesa, pp. 1–114.
82.
United Nations. (2004). UNITED NATIONS GLOBAL E-GOVERNMENT READINESS REPORT 2004: Toward Access for opportunity. Undesa-Unpan, pp. 1–182.
83.
United Nations. (2005). UN Global E-government Readiness Report 2005: From E-government to E-inclusion. Dpadm Desa, 106(6), 1–253.
United States. (2016, May). Executive office of the President. Big data: a report on algorithmic systems, opportunity, and civil rights (Technical report). The White House.
86.
ValédsG.SolarM.AstudilloH.IribarrenM.ConchaG.ViscontiM. (2011). Conception, development and implementation of an e-government maturity model in public agencies. Government Information Quarterly, 28, 176–187.
87.
VincentC. M. (2014). Risks of general artificial intelligence. Journal of Experimental & Theoretical Artificial Intelligence, 26(3), 297–301.
88.
VoldenC. (2006). States as policy laboratories: Emulating success the children’s health insurance program. American Journal of Political Science, 50(2), 294–312.
89.
VoorheesE. M.DangH. T. (2005). Overview of the TREC 2005 question answering track[C]. Proceedings of the Fourteenth Text REtrieval Conference (TREC 2005), New York, pp. 1–15.
90.
WalkerJ. (1969). The diffusion of innovations among the American states. American Political Science Review, 63(3), 880–899.
91.
WalkerR. (2014). Internal and external antecedents of process innovation: A review and extension. Public Management Review, 16(1), 21–44.
92.
WallaceR. S. (2009). The anatomy of A.L.I.C.E. In EpsteinR.RobertsG.BeberG. (Eds.), Parsing the turing test. Springer.
93.
WangY.ZhaoX.ZhangN. (2019). Research on influencing factors of innovation diffusion of chatbots—Based on the event history analysis. E-Government, 2019(12), 75–85.
94.
WeizenbaumJ. (1966). ELIZA—A computer program for the study of natural language communication between man and machine. Communications of the Association for Computing Machinery, 9(1), 36–45.
95.
WelchS.ThompsonK. (1980). The impact of federal incentives on state policy innovation. American Journal of Political Science, 24(4), 715–729.
96.
WestD. M. (2004). E-government and the transformation of service delivery and citizen attitudes. Public Administration Review, 64(1), 15–27.
97.
WestD. M. (2005). Digital government: Technology and public sector performance[M]. Princeton University Press, pp. 71–81.
WirtzB. W.MullerW. M. (2019). An integrated artificial intelligence framework for public management. Public Management Review, 21(7-8), 1076–1100.
101.
WirtzB. W.WeyererJ. C.GeyerC. (2019). Artificial intelligence and the public sector-applications and challenges. International Journal of Public Administration, 42(7), 596–615.
102.
WuJ.ZhangP. (2016). Local government innovation diffusion in China: An event history analysis of a performance-based reform programme. International Review of Administrative Sciences, 84(1), 1–19.
103.
YamaguchiK.FergusonL. (1995). The stopping and spacing of childbirths and their birth-history predictors: Rational-choice theory and event-history analysis. American Sociological Review, 60(2), 272–298.
104.
YudkowskyE. (2008). Artificial intelligence as a positive and negative factor in global risk. In BostromN.CirkovicM. N. (Eds.), Global catastrophic risks (Chap. 15, pp. 308–345). Oxford University Press.
105.
ZhangH.KishoreR.SharmanR.RameshR. (2008). Agile integration modeling language (AIML): A conceptual modeling grammar for agile integrative business information systems. Decision Support Systems, 44(1), 266–284.
106.
ZhangN.GuoX. (2014). Electronic government adoption in China: Multiple research perspectives in the post-informationization age[M]. Tsinghua University Press, pp. 147–149.
107.
ZhangN.LuZ.ShouY. (2017). The dominant role of governing structure in cross-sector collaboration in developing China: Two case studies of information integration in local government one-stop services. Information Technology for Development, 23(3), 554–578.
108.
ZhangN.ZhaoX.ZhangZ.MengQ.TanH. (2017). What factors drive open innovation in China’s public sector? A case study of official document exchange via microblogging (ODEM) in Haining. Government Information Quarterly, 34(1), 126–133.
109.
ZhangY.YangK. (2008). What drives charter school diffusion at the local level: Educational needs or political and institutional forces?Policy Studies Journal, 36(4), 571–591.
110.
ZhuX. (2014). Mandate versus championship: Vertical government intervention and diffusion of innovation in public services in authoritarian China. Public Management Review, 16(1), 117–139.
111.
ZhuX. (2017). Inter-regional diffusion of policy innovation in China: A comparative case study. Asian Journal of Political Science, 25(3), 266–286.
112.
ZhuX.ZhangY. (2016). Political mobility and dynamic diffusion of innovation: The spread of municipal pro-business administrative reform in china. Journal of Public Administration Research and Theory, 26(3), 535–551.
113.
ZhuY. (2012). Policy entrepreneur, civic engagement and local policy innovation in China: Housing monetarisation reform in Guizhou Province. Australian Journal of Public Administration, 71(2), 191–200.
