Mobile AI Chatbots for Citizen Grievance Redressal in Public Service Delivery : A Systematic Literature Review
Authors
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Anuj Kumar
Post-Doctoral Fellow (Corresponding Author), Faculty of Business, Economics and Finance, Perdana University, Suite 5.3, Wisma Chase Perdana Changkat Semantan, Damansara Heights, 50490 Kuala Lumpur, Malaysia. & Faculty of Business and Economics, Al-Quds University, Jerusalem, Palestine (P.O. Box 19356)
https://orcid.org/0000-0002-1205-2794
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Syriac Nellikunnel Devasia
Associate Professor and Dean, Faculty of Business, Economics and Finance, Perdana University, Suite 5.3, Wisma Chase Perdana Changkat Semantan, Damansara Heights, 50490 Kuala Lumpur
https://orcid.org/0000-0001-5646-6342
DOI:
https://doi.org/10.17010/pijom/2026/v19i9/175112Keywords:
artificial intelligence chatbots, citizen grievance redressal, digital government, public service delivery, digital inclusion, human escalation, responsible artificial intelligence.JEL Classification Codes :D83, H83, M15, O33
Publishing Chronology: Paper Submission Date : October 25, 2025 ; Paper sent back for Revision : June 20, 2026 ; Paper Acceptance Date : August 10, 2026 ; Paper Published Online : September 15, 2026.
Abstract
Purpose : The study examined when mobile-accessible artificial intelligence chatbots created public value in citizen grievance redressal and public-service delivery, with particular attention to interaction quality, inclusion, trust, institutional integration, and human escalation.
Design/Methodology/Approach : A systematic literature review of studies published between 2015 and 2026 was conducted using Scopus and Web of Science. Study selection followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework. Thirteen eligible studies were retained after Critical Appraisal Skills Programme-aligned quality assessment and were synthesized through open, axial, and selective coding.
Findings : Three interconnected themes emerged: (a) Digital access creates public value only when interaction produces usable service outcomes; (b) Inclusive use depends on interaction quality, flexibility, and citizen trust; and (c) Responsible automation requires institutional capacity, accountability, and human escalation. Together, the findings established an access–interaction–resolution continuum.
Practical Implications : Public managers were advised to evaluate chatbots through service progression, successful routing, interactional flexibility, and escalation quality rather than response speed alone, while connecting conversational interfaces with language infrastructure and accountable administrative processes.
Originality/Value : The study contributed an integrated access–interaction–resolution perspective that connected chatbot accessibility, interactional inclusion, and administrative responsibility within one public-service process.
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1) Abed, S. S. S. (2024). Understanding the determinants of using government AI-chatbots by citizens in Saudi Arabia. International Journal of Electronic Government Research, 20(1), 1–20. https://doi.org/10.4018/IJEGR.349733
2) Anantha Lakshmi, M., & Saldanha, A. T. (2025). Factors influencing behavioral intentions of senior citizens in adopting mobile financial services. Indian Journal of Marketing, 55(5), 26–44. https://doi.org/10.17010/ijom/2025/v55/i5/175018
3) Bhagate, V. S., & Dam, L. B. (2026). Exploring the outcomes of the faceless assessment—Insights from the Income Tax Department. Prabandhan: Indian Journal of Management, 19(7), 24–43. https://doi.org/10.17010/pijom/2026/v19i7/174665
4) Chakraborty, D. (2024). Revolutionizing travel: The impact of generative AI on personalization and efficiency in the tourism industry. Indian Journal of Marketing, 54(9), 8–24. https://doi.org/10.17010/ijom/2024/v54/i9/174394
5) Chakraborty, D. (2025). Actual usage behavior toward generative AI-based fashion shopping platforms (GAIFSP): Perspectives from DeLone and McLean's information systems success model. Indian Journal of Marketing, 55(1), 29–43. https://doi.org/10.17010/ijom/2025/v55/i1/174686
6) Chen, T., & Gascó-Hernandez, M. (2025). Uncovering the results of AI chatbot use in the public sector: Evidence from US state governments. Public Performance & Management Review, 48(6), 1331–1356. https://doi.org/10.1080/15309576.2024.2389864
7) Chen, T., Gascó-Hernandez, M., & Esteve, M. (2024). The adoption and implementation of artificial intelligence chatbots in public organizations: Evidence from U.S. state governments. The American Review of Public Administration, 54(3), 255–270. https://doi.org/10.1177/02750740231200522
8) Choudhury, R., & Gupta, S. (2025). Augmentation of financial inclusion: A structured review from the Indian perspective. Indian Journal of Finance, 19(5), 58–78. https://doi.org/10.17010//ijf/2025/v19i5/175044
9) Cortés-Cediel, M. E., Segura-Tinoco, A., Cantador, I., & Rodríguez Bolívar, M. 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
10) Dodiya, V. H., Kumar, N., & Tripathi, P. (2026). Digital marketing for DigiYatra adoption: A PLS-SEM study of passenger satisfaction. Prabandhan: Indian Journal of Management, 19(7), 8–23. https://doi.org/10.17010/pijom/2026/v19i7/175088
11) Følstad, A., & Bjerkreim-Hanssen, N. (2024). User interactions with a municipality chatbot—Lessons learnt from dialogue analysis. International Journal of Human–Computer Interaction, 40(18), 4973–4986. https://doi.org/10.1080/10447318.2023.2238355
12) Gujarat Informatics Limited. (2022). Tender for selection of service provider for development of WhatsApp Business API based chatbot solution for Digital Gujarat Services of Department of Science and Technology, Gandhinagar, Govt. of Gujarat. https://gil.gujarat.gov.in/tendercms/TenderDocs/20227261827229.pdf
13) Hemesath, S., & Tepe, M. (2024). Public value positions and design preferences toward AI-based chatbots in e-government: Evidence from a conjoint experiment with citizens and municipal front desk officers. Government Information Quarterly, 41(4), 101985. https://doi.org/10.1016/j.giq.2024.101985
14) Jain, A., Azami, S., Vishnoi, S. K., Pandita, S., & Garg, A. (2026). Strengthening algorithmic safety through responsible and explainable AI: A conceptual framework. Prabandhan: Indian Journal of Management, 19(8), 44–54. https://indianjournalofmanagement.com/index.php/pijom/article/view/175099
15) Ju, J., Meng, Q., Sun, F., Liu, L., & Singh, S. (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
16) Kaun, A., & Männiste, M. (2025). Public sector chatbots: AI frictions and data infrastructures at the interface of the digital welfare state. New Media & Society, 27(4), 1962–1985. https://doi.org/10.1177/14614448251314394
17) Kumar, A., & Banerjee, P. (2025). Leading with intelligence: How AI is reshaping creative innovation in organizations. Prabandhan: Indian Journal of Management, 18(9), 54–62. https://doi.org/10.17010/pijom/2025/v18i9/174843
18) Larsen, A. G., & Følstad, A. (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
19) Li, X., & Wang, J. (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
20) Mahajan, S., Gupta, M., & Chauhan, V. (2025). How may “AI” help you? Analyzing the role of AI-backed chatbot interactions in customer services. Indian Journal of Marketing, 55(4), 69–88. https://doi.org/10.17010/ijom/2025/v55/i4/174929
21) Makasi, T., Nili, A., Desouza, K., & Tate, M. (2020). Chatbot-mediated public service delivery: A public service value-based framework. First Monday, 25(12). https://doi.org/10.5210/fm.v25i12.10598
22) Mohapatra, A. K., Sahoo, A. P., Tripathy, P., Matta, R., & Saxena, A. (2026). Adoption of explainable artificial intelligence in retail investors' decision-making: Evidence from India. Indian Journal of Finance, 20(1), 24–37. https://doi.org/10.17010/ijf/2026/v20i1/175903
23) Othayoth, P. K., & Khanna, S. (2024). Chatbot service quality in banking: Analyzing Indian banking customer perceptions and influence on customer satisfaction and value. Indian Journal of Marketing, 54(2), 44–65. https://doi.org/10.17010/ijom/2024/v54/i2/173474
24) Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., . . . Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
25) Pahari, S., Polisetty, A., Sharma, S., Jha, R., & Chakraborty, D. (2023). Adoption of AI in the banking industry: A case study on Indian banks. Indian Journal of Marketing, 53(3), 26–41. https://doi.org/10.17010/ijom/2023/v53/i3/172654
26) Papadopoulos, T., Alexopoulos, C., & Charalabidis, Y. (2025). Evaluating chatbot architectures for public service delivery: Balancing functionality, safety, ethics, and adaptability. Frontiers in Political Science, 7, 1601440. https://doi.org/10.3389/fpos.2025.1601440
27) Pawar, S. D., & Chavan, P. P. (2026). Bridging the gap: Financial literacy as a tool for inclusive growth among economically disadvantaged groups in selected regions of India. Indian Journal of Finance, 20(6), 46–67. https://doi.org/10.17010/ijf/2026/v20i6/175559
28) Press Information Bureau. (2025). The Department of Administrative Reforms and Public Grievances (DARPG) released the 42nd Monthly Report on Centralized Public Grievance Redress and Monitoring System (CPGRAMS) for Central Ministries/ Departments performance for the month of October, 2025. https://www.pib.gov.in/PressReleasePage.aspx?PRID=2188324®=48&lang=2
29) Press Information Bureau. (2026a). Government expands citizen-centric digital public service delivery through UMANG and DigiLocker. Ministry of Electronics and Information Technology, Government of India. https://www.pib.gov.in/PressReleasePage.aspx?PRID=2298116&lang=2®=48
30) Press Information Bureau. (2026b). Artificial intelligence for culture and languages. https://static.pib.gov.in/WriteReadData/specificdocs/documents/2026/feb/doc202629784101.pdf
31) Press Information Bureau. (2026c). Digital India BHASHINI Division: National Hub for Language Technologies powers end-to-end AI at population scale. Ministry of Electronics and Information Technology, Government of India. https://www.pib.gov.in/PressReleasePage.aspx?PRID=2239132&lang=1®=1
32) Raju, G., & Gupta, S. (2026). Exploring enablers and barriers of M-banking adoption for financial inclusion in rural India. Indian Journal of Finance, 20(5), 56–76. https://doi.org/10.17010/ijf/2026/v20i5/175463
33) Sobhan, S., Oishi, O. V., Hossain, M. M., Sultana, M., & Hossain, M. J. (2026). Tourists' willingness to use AI-enabled chatbots: Extending the technology acceptance model. Indian Journal of Marketing, 56(3), 65–85. https://doi.org/10.17010/ijom/2026/v56/i3/175245
34) Song, Y., Natori, T., & Yu, X. (2025). Trusting humans or bots? Examining trust transfer and algorithm aversion in China's e-government services. Administrative Sciences, 15(8), 308. https://doi.org/10.3390/admsci15080308
35) Unique Identification Authority of India. (2026). Grievance & feedback. https://uidai.gov.in/en/grievance-feedback
