Authors : Ramer J. Laylo; Rolaida L. Sonza

Volume/Issue : Volume 11 - 2026, Issue 2 - February

Google Scholar : https://tinyurl.com/2e56wypu

Scribd : https://tinyurl.com/hyzv8zza

DOI : https://doi.org/10.38124/ijisrt/26feb1447

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Electric cooperatives in the Philippines remain vital to rural electrification, yet many continue to rely on manual complaint-handling methods such as walk-in transactions, phone calls, and handwritten logbooks. These practices often result in delayed responses, fragmented documentation, and weak accountability, which in turn erode consumer trust. This study designed, developed, and evaluated a Consumer Complaint Management System (CCMS) enhanced with artificial intelligence (AI)-based segmentation to address these challenges. The system was built using the Agile Development Model, allowing iterative refinement through continuous feedback from IT experts and end-users. Core features include mobile and web-based complaint submission, automated categorization and prioritization using supervised machine learning, geotagenabled reporting, workflow dashboards for staff and management, and role-based access control to ensure data security. Evaluation was conducted in two phases: IT experts assessed the system using ISO/IEC 25010 software quality standards, while Member-Consumer-Owners (MCOs) and cooperative personnel evaluated functional suitability, performance efficiency, usability, and overall acceptability. Results demonstrated excellent ratings across all quality dimensions, with end-users reporting faster complaint resolution, improved accessibility, and high satisfaction with the system’s usability. The findings confirm that AI-driven segmentation enhances complaint management efficiency, reduces delays caused by manual routing, and strengthens transparency and accountability in service delivery. Beyond improving daily operations, the CCMS supports compliance with government mandates such as Republic Act No. 11032 and NEA reporting requirements. Ultimately, the system fosters stronger relationships between cooperatives and their consumers by ensuring that complaints are systematically recorded, properly addressed, and resolved in a timely manner.

Keywords : Consumer Complaint Management System, Artificial Intelligence, Electric Cooperatives, Agile Development, ISO/IEC 25010.

References :

1. Abedin, M., & Sultana, R. (2020). Artificial intelligence in rural service management: Enhancing complaint handling through mobile technology. Journal of Digital Governance, 12(2), 55–68. https://scholar.google.com/scholar?q=Artificial+intelligence+in+rural+service+management+complaint+handling
2. Abdul-Hamid, H., Hassan, S., & Said, R. (2020). Artificial intelligence adoption and customer engagement in public service platforms. International Journal of Service Science, 11(3), 201–217. https://scholar.google.com/scholar?q=Artificial+intelligence+adoption+customer+engagement+public+service+platforms
3. Almeida, F., & Correia, A. (2021). Digital transformation and organizational performance: The role of information systems. Journal of Information Systems Management, 38(4), 310–322. https://scholar.google.com/scholar?q=Digital+transformation+organizational+performance+information+systems+Almeida+Correia
4. Alvarez, J. P. (2022). Digitalization challenges in Philippine electric cooperatives. Philippine Journal of Energy Policy, 7(2), 41–56. https://scholar.google.com/scholar?q=Digitalization+challenges+Philippine+electric+cooperatives
5. Azzam, R., Hassan, N., & Noor, M. (2021). Artificial intelligence-driven ticketing systems for public services. International Journal of Information Systems Innovation, 13(2), 66–81. https://scholar.google.com/scholar?q=AI+driven+ticketing+systems+public+services
6. Bautista, J., & Peralta, M. (2020). Digital transformation initiatives of Philippine electric cooperatives. Philippine Journal of Energy Studies, 18(1), 33–47. https://scholar.google.com/scholar?q=Digital+transformation+Philippine+electric+cooperatives+Bautista+Peralta
7. Bautista, L., & Ramos, D. (2023). Analytics-driven decision support systems in electric utilities. Journal of Utility Management and Technology, 15(1), 89–104. https://scholar.google.com/scholar?q=Analytics+driven+decision+support+electric+utilities
8. Chen, L., & Li, X. (2021). Automated complaint segmentation using machine learning techniques. Journal of Artificial Intelligence Applications, 10(4), 55–68. https://scholar.google.com/scholar?q=Automated+complaint+segmentation+machine+learning
9. Chen, L., Zhang, Y., & Wu, H. (2022). Natural language processing applications for consumer complaint analysis in smart energy systems. International Journal of Artificial Intelligence and Applications, 11(3), 102–118. https://scholar.google.com/scholar?q=NLP+consumer+complaint+analysis+smart+energy+systems
10. David, R., Flores, K., & San Juan, P. (2021). Digital complaint management systems: Bridging customer relations and technology in local government units. Journal of Philippine Information Systems, 10(2), 78–90. https://scholar.google.com/scholar?q=Digital+complaint+management+local+government+Philippines
11. DeLone, W. H., & McLean, E. R. (2016). Information systems success measurement. Foundations and Trends in Information Systems, 2(1), 1–116. https://scholar.google.com/scholar?q=DeLone+McLean+Information+Systems+Success+Model
12. Department of Information and Communications Technology (DICT). (2022). Philippine e-Government Master Plan 2022. DICT. https://www.dict.gov.ph
13. Department of Information and Communications Technology (DICT). (2023). Digital governance initiatives and innovation roadmap. DICT. https://www.dict.gov.ph
14. Elmasri, R., & Navathe, S. B. (2020). Fundamentals of database systems (7th ed.). Pearson. https://scholar.google.com/scholar?q=Elmasri+Navathe+Fundamentals+of+Database+Systems
15. Ferraiolo, D., Kuhn, D. R., & Chandramouli, R. (2022). Role-based access control (2nd ed.). Artech House. https://scholar.google.com/scholar?q=Ferraiolo+Role+Based+Access+Control
16. Gatchalian, R., & Llamas, M. (2022). Transparency and trust in digital public service platforms. Philippine Journal of Public Administration, 66(1), 97–114. https://scholar.google.com/scholar?q=Transparency+trust+digital+public+service+Philippines
17. Garcia, R., Mendoza, L., & Flores, C. (2021). Challenges in implementing digital complaint tracking among rural utilities. Energy Management Review, 16(3), 23–40. https://scholar.google.com/scholar?q=Digital+complaint+tracking+rural+utilities
18. Jurafsky, D., & Martin, J. H. (2023). Speech and language processing (3rd ed.). Pearson. https://scholar.google.com/scholar?q=Jurafsky+Martin+Speech+Language+Processing
19. Kumar, N., & Gupta, R. (2021). Using sentiment analysis for effective customer complaint triage in AI systems. Journal of Information and Decision Sciences, 13(4), 87–98. https://scholar.google.com/scholar?q=Sentiment+analysis+customer+complaint+triage
20. Kumar, R., & Singh, P. (2020). Dual-platform systems for public service applications. International Journal of Computing and Digital Systems, 9(2), 145–156. https://scholar.google.com/scholar?q=Dual+platform+systems+public+service
21. Labrador, M., & Castillo, J. (2020). Accessibility barriers in public utility complaint reporting. Philippine Journal of Social Services, 12(1), 61–74. https://scholar.google.com/scholar?q=Accessibility+barriers+complaint+reporting+Philippines
22. Lee, S. (2021). Intelligent automation in customer service: Enhancing public utility responsiveness. Journal of Smart Systems and Technologies, 19(2), 112–128. https://scholar.google.com/scholar?q=Intelligent+automation+customer+service+public+utilities
23. Lewis, J. R., & Sauro, J. (2022). Quantifying the user experience (2nd ed.). Morgan Kaufmann. https://scholar.google.com/scholar?q=Lewis+Sauro+Quantifying+the+User+Experience
24. Mantaring, A. V., Espinoza, M. A. P., & Gabriel, A. G. (2021). Complaint management in public sector organizations in the Philippines. Philippine Journal of Public Administration, 65(1), 45–62. https://scholar.google.com/scholar?q=Complaint+management+public+sector+Philippines+Mantaring
25. Mehta, S. (2022). Artificial intelligence in power distribution complaint systems. Energy Informatics Journal, 5(1), 1–14. https://scholar.google.com/scholar?q=AI+power+distribution+complaint+systems
26. Müller, J., & Schneider, F. (2019). Integrating geotagging and predictive analytics in European consumer service platforms. European Journal of Technology and Society, 8(1), 73–88. https://scholar.google.com/scholar?q=Geotagging+predictive+analytics+consumer+service+platforms
27. National Electrification Administration (NEA). (2021–2023). EC modernization framework and annual reports. NEA. https://www.nea.gov.ph
28. National Privacy Commission. (2021). Data Privacy Act of 2012 IRR. NPC. https://privacy.gov.ph
29. Pressman, R. S., & Maxim, B. R. (2020). Software engineering: A practitioner’s approach (9th ed.). McGraw-Hill Education. https://scholar.google.com/scholar?q=Pressman+Maxim+Software+Engineering
30. Putra, A., Wijaya, R., & Santoso, D. (2023). AI-based feedback systems in rural electric cooperatives. International Journal of Smart Infrastructure, 6(2), 55–70.https://scholar.google.com/scholar?q=AI+feedback+systems+rural+electric+cooperatives
31. Rahman, M., & Dey, T. (2023). Artificial intelligence in complaint handling and service automation. International Journal of Advanced Computer Science, 15(2), 77–92. https://scholar.google.com/scholar?q=AI+complaint+handling+service+automation
32. Richey, R. C., & Klein, J. D. (2019). Developmental research: Studies of instructional design and development. In Handbook of Research on Educational Communications and Technology. Springer. https://scholar.google.com/scholar?q=Richey+Klein+Developmental+Research
33. Sah, S., & Gregor, S. (2022). Intelligent triage systems for service management. Journal of Information Technology Theory and Application, 23(1), 41–58. https://scholar.google.com/scholar?q=Intelligent+triage+systems+service+management
34. Shneiderman, B., et al. (2021). Designing the user interface (6th ed.). Pearson. https://scholar.google.com/scholar?q=Shneiderman+Designing+the+User+InterfaceSommerville, I. (2021). Software engineering (10th ed.). Pearson. https://scholar.google.com/scholar?q=Sommerville+Software+Engineering
35. Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology. MIS Quarterly, 36(1), 157–178. https://scholar.google.com/scholar?q=UTAUT+Venkatesh
36. Whitman, M. E., & Mattord, H. J. (2021). Principles of information security (7th ed.). Cengage Learning. https://scholar.google.com/scholar?q=Whitman+Mattord+Information+Security
37. Zhang, X., Li, Q., & Zhou, M. (2021). User-centered AI systems. International Journal of Human–Computer Interaction, 37(8), 690–707. https://scholar.google.com/scholar?q=User+centered+AI+systems
38. Zhou, X., Li, Y., & Wang, H. (2021). Artificial intelligence applications in public service complaint management. Journal of E-Government Studies, 9(3), 101–118. https://scholar.google.com/scholar?q=AI+public+service+complaint+management