การพัฒนาระบบสนับสนุนการตัดสินใจบนฐานข้อมูลบูรณาการเพื่อการชี้เป้าและติดตามการช่วยเหลือครัวเรือนยากจนเชิงพื้นที่: กรณีศึกษาจังหวัดนครราชสีมา

Chutrapee Popitikul

doi:10.65205/jcct.2026.e4212

Authors

Chutrapee Popitikul Computer Education Program, Faculty of Science and Technology, Nakhon Ratchasima Rajabhat University, Nakhon Ratchasima 30000, Thailand https://orcid.org/0009-0006-6673-6071

DOI:

https://doi.org/10.65205/jcct.2026.e4212

Keywords:

Decision Support System, Integrated Database, Poverty Targetin, TPMAP, Evidence-Based Decision Making

Abstract

This study set out to (1) build an integrated-data Decision Support System that targets, screens, and tracks assistance for poor households; (2) examine what happened once it was deployed area-wide; and (3) gauge user satisfaction alongside the system quality and suitability judged by experts. Development followed a system-development approach within Participatory Action Research and the System Development Life Cycle. The system linked three databases, TPMAP, ThaiQM, and PPP-Connext, through an API tied to the Government Data Catalog and the provincial City Data Platform, then added a standardized two-track verification mechanism and a three-level monitoring and feedback process (tambon, district, province). Two instruments were used: a five-point satisfaction questionnaire for 56 trained officer-users, and an expert-assessment form for five information-technology experts. Five experts confirmed content validity (IOC 0.80–1.00), and internal-consistency reliability was Cronbach’s $\alpha$ = 0.89 and 0.93. Once deployed across 657 agencies covering 21,521 households, the redesigned process replaced scattered offline records with a single real-time platform. Assistance reached 4,613 households, and every identified hardest-hit (“Living-with-Difficulty”) household was referred into state welfare. Users rated overall satisfaction high ( $\bar{x}$ = 4.34); experts rated overall quality and suitability high ( $\bar{x}$ = 4.44), though processing speed scored lowest ( $\bar{x}$ = 4.00). Two caveats matter. The study did not measure changes in household income or poverty status, and the $\alpha$ values describe the instruments, not how the system performs. Even so, the evidence suggests that pulling fragmented poverty data into one DSS can sharpen targeting, shorten the time needed to reach information, and make assistance more transparent, giving other provinces a model they can adapt where data readiness is comparable.

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