Customer importance vs service performance: a multifaceted investigation in the Indonesia halal certification
Bibliographic record
Abstract
Purpose This study aims to measure the consumer satisfaction index (CSI) and show the gap between importance and performance attributes in halal certification services. Design/methodology/approach This study uses CSI, paired-sample t-tests and importance performance analysis (IPA) with measurement indicators. The sample size was 2,743, drawn from 34 provinces in Indonesia. Findings The results showed that the CSI value was 88.6%, but the results of the paired sample t-test showed a difference and gap between importance and performance, with an average value of 4.27%. According to IPA, establishing service requirements, ensuring ease of access to information on halal certification services and streamlining consumer complaint submission were attributes of high consumer importance and low performance. Research limitations/implications This research has limited access to foreign business actors; therefore, the research sample only accommodates domestic business actors. Practical implications This study emphasizes the need for policymakers to formulate customer-centric policies by evaluating customer satisfaction and service provider performance based on customer importance and service performance. Originality/value This study encourages the adoption of public service attributes to measure customer satisfaction in the halal certification process. In addition, it designed a service satisfaction evaluation model by sequentially elaborating the CSI, paired sample t-test and IPA approaches into one unit to reveal the satisfaction values.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".