The Development of a Web-Based Strategic Pricing Application to Support the Sustainability of Creative-Based SMEs
Bibliographic record
Abstract
This study analyzes the theoretical and practical effects of Perceived Usefulness and Perceived Ease of Use on Attitude Toward Using and Behavioral Intention to use the web-based pricing application Mersyprice, based on the Technology Acceptance Model theory.Mersyprice is a web-based application to support strategic pricing for Small and Medium Enterprises (SMEs) in the creative sector.Data was collected through an online Focus Group Discussion (FGD) using Zoom to introduce the use of Mersyprice in supporting strategic pricing for creative SMEs.Participants then completed a questionnaire using a Google form.Data analysis was carried out in 2 stages.The first stage, to answer theoretical objectives, Structural Equation Modeling was used.While the second stage, to answer practical purposes, the Analytical Hierarchy Process was used.The firststage of analysis showed that Perceived Usefulness and Perceived Ease of Use have a significant impact on Behavioral Intention to use the Mersyprice application via Attitude Toward Using it.Respondents considered that the "Ease of Use" variable was more important.The second stage of analysis revealed that 47% of creative SMEs intend to use Mersyprice, while 35% are considering using it.It can be concluded that Mersyprice is useful in supporting strategic pricing by creative sector of SMEs.For future research it is important to develop the Mersyprice in a more user friendly so that it can be used more widely.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".