The effects of perceived ease of use, electronic word of mouth and content marketing on purchase decision
Bibliographic record
Abstract
Purchasing decisions on the Traveloka application has experienced a significant decline since the Covid-19 pandemic outbreak. Ticket returns and refunds that occur due to travel restrictions have resulted in a decline in Traveloka's brand image. This study aims to analyze how brand image mediates the effect of perceived ease of use, electronic word of mouth and content marketing towards ticket purchasing decisions on the Traveloka application which was conducted on 130 respondents using the Traveloka application. The research was conducted in June 2021 with data analysis using SmartPLS 3.2.0 software. The results show that perceived ease of use had a negative impact on purchasing decisions, either directly or indirectly through brand image. Electronic word of mouth had a positive impact on purchasing decisions either directly or indirectly through brand image. Content marketing had a negative and significant impact on purchasing decisions, while indirectly through brand image had a positive and significant impact. The role of brand image was very important in increasing the effect of perceived ease of use, electronic word of mouth and content marketing towards purchasing decisions.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.005 | 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".