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Record W4382204973 · doi:10.33480/pilar.v19i1.3948

EVALUATION OF USER SATISFACTION USING THE PIECES FRAMEWORK IN THE TEMAN BUS APPLICATION

2023· article· en· W4382204973 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJurnal Pilar Nusa Mandiri · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior and Marketing Influence
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsChristian ministryPublic transportControl (management)Service (business)Computer scienceTransport engineeringBusinessEngineeringMarketing

Abstract

fetched live from OpenAlex

The TEMAN BUS program employs technology to improve road-based public transportation in urban areas. The Ministry of Transportation of the Republic of Indonesia implements digital transformation by developing the TEMAN BUS application to support TEMAN BUS transportation services. Passengers will find it more straightforward with this application to access the TEMAN BUS travel route, view information about the departure timetable, and observe bus arrivals in real time. To evaluate an application's effectiveness, it is necessary to assess the user's impression when the program is launched. This study uses the PIECES Framework, which has six variables Performance, Information, Economics, Control and Security, Efficiency, and Service, to assess how users perceive the TEMAN BUS application. The findings of this study were derived from the perceptions of respondents, who felt that information and data performance, control and security, service, and user satisfaction were not good, and from the findings of hypothesis testing, which suggested that information and data performance and user satisfaction were unrelated.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.260

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.064
GPT teacher head0.332
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it