Perceived of ease of use and usefulness: Empirical evidence of behavioral intention to use QR code technology on Indonesian commuter lines
Bibliographic record
Abstract
This study aims to estimate the factors determining the perceived behavioral Intention to use the QR code on a smartphone in the commuter line tap-in tap-out ticketing process as an alternative payment. The rapid growth of information technology in the last two decades had become a factor that encouraged individuals and groups to utilize information technology from devices or technological tools as effectively and efficiently as possible to facilitate the activities and business processes being carried out. This research used the probability sampling technique with a random sampling of 100 commuter line passengers. In addition, this research used the data analysis technique of the Structural Equation Model-SmartPLS3.0. The results indicated that perceived compatibility and enjoyment significantly affected the perceived ease of use and usefulness in the consumer's behavioral Intention to use QR Code technology on smartphones as a substitution for purchasing commuter train tickets. However, the three other variables, perceived convenience, self-efficacy, and enjoyment, do not significantly influence the usefulness of using QR Code Technology on smartphones as an alternative for purchasing commuter tickets, and neither do the technological knowledge and perceived compatibility.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".