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Cited by 65

The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.

Citizen-Centric Governance: Enhancing Citizen Engagement through Artificial Intelligence Tools

Pislaru, Marius, Vlad, Ciprian Sorin, Ivascu, Larisa, Mircea, Iulia Ioana·2024·Sustainability
DOI 10.3390/su16072686
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Abstract

The public sector presents important steps for digital transformation. Digital transformation uses a series of tools and methods to improve the relationship with citizens and improve benefits. This paper explores the role of artificial intelligence (AI) in governance processes and provides public institutions with insight regarding the impact of integrating chatbot communication tools when interacting with citizens. The present research provides an analysis of the socio-economic factors that determine the use of artificial intelligence tools, i.e., the propensity to interact more with the public administration as a result of improved communication through virtual assistants, and highlights the implications of AI in improving services towards civil society by determining the degree of satisfaction on aspects such as reduced waiting times in queues, access to information regardless of the traditional working hours of civil servants, quicker execution of operations, et al. The results, derived from an analysis of 507 sets of responses obtained from an online questionnaire, indicate that a number of variables, such as residential environment, employment status, household income and education level, significantly impact the effectiveness of artificial intelligence in mediating citizen communication with government.

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Research works printed but not linked: 42

  1. Prabhu, C.S.R. (2004). E-Governance: Concepts and Case Studies, Prentice Hall of India.
  2. Brown, 2005, Electronic government and public administration, International review of administrative sciences, Int. Rev. Adm. Sci., 71, 241, 10.1177/0020852305053883 · DOI
  3. Bannister, 2012, Defining e-Governance, e-Serv. J., 8, 3, 10.2979/eservicej.8.2.3 · DOI
  4. 2008, Good governance: Characteristics, methods and the Austrian examples, Transylv. Rev. Adm. Sci., 4, 26
  5. Lykidis, I., Drosatos, G., and Rantos, K. (2021). The Use of Blockchain Technology in e-Government Services. Computers, 10. · DOI
  6. Vrabie, C. (2023). E-Government 3.0: An AI Model to Use for Enhanced Local Democracies. Sustainability, 15. · DOI
  7. Oliveira, T.A., Oliver, M., and Ramalhinho, H. (2020). Challenges for Connecting Citizens and Smart Cities: ICT, E-Governance and Blockchain. Sustainability, 12. · DOI
  8. Paananen, 2023, Towards Interactive Governance: Embedding Citizen Participation in Local Government, Adm. Soc., 55, 1529, 10.1177/00953997231177220 · DOI
  9. Sigwejo, 2016, A citizen-centric framework for assessing E-government effectiveness, Electron. J. Inf. Syst. Dev. Ctries., 72, 1, 10.1002/j.1681-4835.2016.tb00542.x · DOI
  10. Misra, D.C. (2006, January 2–3). Defining e-government: A citizen-centric criteria-based approach. Proceedings of the 10th National Conference on e-Governance, Bhopal, India. Available online: http://www.10thnationalegovconf.in/14.pdf.
  11. King, 2007, Transformational Government?, The Role of Information technology in delivering citizen-centric local public services. Local Gov. Stud., 33, 333
  12. Murtarelli, 2021, A conversation-based perspective for shaping ethical human-machine interactions: The particular challenge of chatbots, J. Bus. Res., 129, 927, 10.1016/j.jbusres.2020.09.018 · DOI
  13. Michels, 2017, Examining citizen participation: Local participatory policymaking and democracy revisited, Local Gov. Stud., 43, 875, 10.1080/03003930.2017.1365712 · doi:10.1080/03003930.2017.1365712
  14. Ianniello, 2019, Obstacles and solutions on the ladder of citizen participation: A systematic review, Public Manag. Rev., 21, 21, 10.1080/14719037.2018.1438499 · DOI
  15. Rouet, G., and Côme, T. (2023). Participatory and Digital Democracy at the Local Level, Springer. Contributions to Political Science. · doi:10.1007/978-3-031-20943-7
  16. Hasan, I., Rizvi, S., Jain, S., and Huria, S. (2021, January 17–19). The AI enabled Chatbot Framework for Intelligent Citizen-Government Interaction for Delivery of Services. Proceedings of the 2021 8th International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India.
  17. Henman, 2020, Improving public services using artificial intelligence: Possibilities, pitfalls, governance, Asia Pac. J. Public Adm., 42, 209
  18. Mahapatra, R.P., Sharma, N., Trivedi, A., and Aman, C. (2012, January 5–7). Adding interactive interface to E-Government systems using AIML based chatterbots. Proceedings of the 2012 CSI Sixth International Conference on Software Engineering (CONSEG), Indore, India. · DOI
  19. Chen, 2021, AI-based self-service technology in public service delivery: User experience and influencing factors, Gov. Inf. Q., 38, 101520, 10.1016/j.giq.2020.101520 · DOI · Paper page
  20. Tschopp, M., and Ruef, M. (2023, November 20). On Trust in AI—A Systemic Approach. Available online: https://www.researchgate.net/publication/336850230_On_Trust_in_AI_-_A_Systemic_Approach.
  21. Androutsopoulou, A., Karacapilidis, N., and Charalabidis, Y. (2017). E-Democracy—Privacy-Preserving, Secure, Intelligent E-Government Services, Springer.
  22. Khatri, 2018, Alexa Prize—State of the Art in Conversational AI, AI Mag., 39, 40
  23. Huang, L., Liang, X., Li, L., Xiao, H., and Xie, F. (2023). The Impact of Internet Use on the Well-Being of Rural Residents. Agriculture, 13. · DOI
  24. Bauer, P. (2018). Unemployment, Trust in Government and Satisfaction with Democracy: An Empirical Investigation. Sociol. Res. A Dyn. World, 4. · DOI
  25. 1979, Socioeconomic Status, Unemployment Experience, and Political Participation: A Disentangling of Main and Interaction Effects, Political Behav., 1, 361, 10.1007/BF00989809 · DOI
  26. Giustozzi, C., and Gangl, M. (2021). Unemployment and political trust across 24 western democracies: Evidence on a welfare state paradox. Acta Sociol., 64. · DOI
  27. Bilos, A., and Budimir, B. (2023, November 20). How much do we trust in AI? Exploring the impact of artificial intelligence on user trust levels. Available online: https://www.researchgate.net/publication/374068661_HOW_MUCH_DO_WE_TRUST_IN_AI_EXPLORING_THE_IMPACT_OF_ARTIFICIAL_INTELLIGENCE_ON_USER_TRUST_LEVELS.
  28. Mircea, 2023, Drivers acceptance of fully automated vehicles, Annals of the Academy of Romanian Scientists, Ser. Eng. Sci., 15, 99
  29. Abraham, 2017, Advanced Driver Assistance Systems (ADAS): A Consideration of Driver Perceptions on Training, Usage & Implementation, Proc. Hum. Factors Ergon. Soc. Annu. Meet., 61, 1954, 10.1177/1541931213601967 · DOI
  30. Meuleman, L. (2021). Public Administration and Governance for the SDGs: Navigating between Change and Stability. Sustainability, 13. · DOI
  31. Mieczysław, L., Sawicka, A., and Weichbroth, P. (2019). IFIP International Workshop on Artificial Intelligence for Knowledge Management, Springer International Publishing.
  32. Shah, W.U.H., Yasmeen, R., Sarfraz, M., and Ivascu, L. (2023). The Repercussions of Economic Growth, Industrialization, Foreign Direct Investment, and Technology on Municipal Solid Waste: Evidence from OECD Economies. Sustainability, 15. · DOI
  33. Bhatta, D.D., Sarfraz, M., Ivascu, L., and Pislaru, M. (2023). The Nexus of Corporate Affinity for Technology and Firm Sustainable Performance in the Era of Digitalization: A Mediated Model. Sustainability, 15. · DOI
  34. Mills, D., Pudney, S., Pevcin, P., and Dvorak, J. (2022). Evidence-Based Public Policy Decision-Making in Smart Cities: Does Extant Theory Support Achievement of City Sustainability Objectives?. Sustainability, 14. · DOI
  35. Sun, J., Sarfraz, M., Turi, J.A., and Ivascu, L. (2022). Organizational Agility and Sustainable Manufacturing Practices in the Context of Emerging Economy: A Mediated Moderation Model. Processes, 10. · DOI
  36. Heinrichs, H., and Laws, N. (2021). Sustainable Public Administration. Sustainability, 13. · doi:10.3390/su13116382
  37. Herghiligiu, I.V., Robu, I.-B., Pislaru, M., Vilcu, A., Asandului, A.L., Avasilcăi, S., and Balan, C. (2019). Sustainable Environmental Management System Integration and Business Performance: A Balance Assessment Approach Using Fuzzy Logic. Sustainability, 11. · DOI
  38. Haddara, 2023, Empowering homes with intelligence: An investigation of smart home technology adoption and usage, Internet Things, 24, 100944, 10.1016/j.iot.2023.100944 · DOI
  39. Liu, J., Puah, C.-H., Arip, M.A., and Jong, M.-C. (2023). Impacts of Digital Financial Inclusion on Urban–Rural Income Disparity: A Comparative Research of the Eastern and Western Regions in China. Economies, 11. · DOI
  40. Zellou, 2021, Age and Gender Related Differences in Speech alignment toward humans and Voice-AI, Front. Commun., 5, 600361, 10.3389/fcomm.2020.600361 · DOI
  41. Omrani, 2022, To trust or not to trust? An assessment of trust in AI-based systems: Concerns, ethics and contexts, Technol. Forecast. Soc. Chang., 181, 121763, 10.1016/j.techfore.2022.121763 · DOI · Paper page
  42. Carradore, 2022, Peoples attitudes towards the use of robots in the social services: A multilevel analysis using Eurobarometer data, Int. J. Soc. Robot., 14, 845, 10.1007/s12369-021-00831-4 · DOI

Cited by

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Metadata

Authors
Pislaru, M., Vlad, C. S., Ivascu, L., & Mircea, I. I.
Affiliations
  • Department of Engineering Management, Faculty of Industrial Design and Business Management, “Gheorghe Asachi” Technical University of Iasi, 700050 Iasi, Romania
  • Department of Management, Research Center for Engineering and Management (RCEM), Faculty of Management in Production and Transportation, Politehnica University of Timișoara, 300006 Timișoara, Romania
  • Faculty of Transportation, National University of Science and Technology Politehnica Bucharest, 060042 Bucharest, Romania
ORCID
Ivascu, Larisa
Venue
Sustainability
Publisher
MDPI AG
Published
2024-03-25
Volume
16
Issue
7
Pages
2686
ISSN
2071-1050
DOI
10.3390/su16072686
Cited by
65
References
11
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)

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