PENGARUH VARIETY SEEKING DAN E-WOM TERHADAP BRAND SWITCHING PADA PENGGUNA SAMSUNG KE IPHONE (Studi Pada Generasi Z di Kota Semarang)
Bibliographic record
Abstract
Smartphones are the result of technological developments that are progressing rapidly. The rapid progress of information technology encourages smartphone manufacturers to compete to create superior products. This makes consumers have many choices in choosing a smartphone. According to IDC data in the 3rd quarter of 2021/2022 and the Top Brand Award for Teens in 2022/2023, Samsung is in the top position. However, its YoY and index have decreased, which means that there is a decrease in shipments, market share, mind share, and commitment share. In contrast, the iPhone actually experienced a significant increase. The sample in this study amounted to 100 Generation Z respondents in Semarang City using Samsung smartphones who switched brands to iPhones. Data analysis in this study was processed using SPSS version 26. This study produces findings that show that variety seeking and e-wom partially and simultaneously have a positive and significant effect on brand switching from Samsung to iPhone. This study suggests that Samsung can increase product launches and innovative features to reduce the level of variety seeking and increase e-wom activities through providing product information and cooperation with users to share positive experiences in order to increase its competitiveness on social media.
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.
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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".