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The Dynamics of Client Satisfaction in Frontline Services

Ian Jay Gruyal, Judith Sanchez
Psychology and Education: A Multidisciplinary Journal · 2026-03-23 · DOI 10.70838/pemj.530702
Token #7422
✓ Verified on SciChain. The record below hashes to exactly the content hash stored on chain for token #7422.
Token#7422 of SciMatic Articles (ERC-1155), fixed supply 1,000,000
Contract0x0E596B4bb924937aA35D9b342A90347ed6F05eF8
Mint transaction0xde749dbd879f5fe2b60a5a65b550a28c64c93c091cd599ba4b711d7ab9e1d5e7 · block 12110077
Minted2026-10-02 11:35 UTC
Hash on chain0x226cc4fe975762f0b6604c6b601b9683e59b76bb8322870cedb04fdea37324d2
SHA-256 of the record0x226cc4fe975762f0b6604c6b601b9683e59b76bb8322870cedb04fdea37324d2
Article today Matches the anchored record.

Allocation at minting

HolderWalletMintedHolds now
SciMatic (10%) 0xc35709AB…277732 100,000 100,000
Ian Jay Gruyal 0xd113A54D…1400f6 450,000 450,000
Judith Sanchez 0xF6831502…6d4353 450,000 450,000

The anchored record

Canonical JSON (keys sorted, no spaces, UTF-8). Its SHA-256 is the hash on chain.

{"abstract":"This study investigates the determinants of client satisfaction in frontline public service delivery at the Registry of Deeds in Patin-ay, Prosperidad, Agusan del Sur. Given the critical role of frontline services in shaping public trust, the research examines how client expectations, service consistency, communication effectiveness, efficiency, transparency, and technology integration influence overall satisfaction. A quantitative, descriptive-correlational research design was employed, and the data were collected in April using a researcher-developed and expert-validated questionnaire measured on a five-point Likert scale. Respondents were selected through convenience sampling from clients actively availing registry services. Instrument reliability was confirmed through pilot testing using Cronbach’s alpha. Data analysis involved descriptive statistics to summarize client perceptions and inferential techniques—specifically, Pearson correlation and multiple regression—to identify significant predictors of satisfaction. Ethical standards, including informed consent, voluntary participation, and confidentiality, were strictly observed. Results indicate a consistently high level of client satisfaction, with overall ratings falling within the “Strongly Agree” category. Client expectations (M = 4.657) and perceived service quality (M = 4.590) emerged as the strongest contributors to satisfaction. Service consistency (M = 4.414), efficiency (M = 4.473), communication effectiveness (M = 4.518), and transparency (M = 4.533) were also found to be statistically significant predictors. Technology integration (M = 4.419) showed strong positive relationships with both service quality and operational efficiency. However, feedback mechanisms recorded relatively lower ratings (M = 3.920–3.947), indicating gaps in institutional responsiveness to client input. While frontline service delivery is highly effective, strengthening feedback systems and institutionalizing sustained client engagement are essential to maintaining service excellence and deepening public trust in government institutions.","abstract_source":"original","authors":["Ian Jay Gruyal","Judith Sanchez"],"date":"2026-03-23","doi":"10.70838/pemj.530702","id":7422,"issue":7,"journal":{"issn":"2822-4353","name":"Psychology and Education: A Multidisciplinary Journal"},"pdf_sha256":"33eb74cc8f3ab845730a0bfce32ac4ca2e4e310d12f918b866c0560f24ae5977","schema":"scimatic-article/1","title":"The Dynamics of Client Satisfaction in Frontline Services","volume":53}

Check it yourself

1. The record, from the token's own metadata:

printf '%s' "$(curl -s https://scimatic.org/nft/articles/7422.json | jq -r .canonical_record)" | sha256sum

2. The hash stored on chain (contentHash(7422)):

curl -s https://rpc.scimatic.net -H 'content-type: application/json' \
  -d '{"jsonrpc":"2.0","id":1,"method":"eth_call","params":[{"to":"0x0E596B4bb924937aA35D9b342A90347ed6F05eF8","data":"0xf3e0c2900000000000000000000000000000000000000000000000000000000000001cfe"},"latest"]}'

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