Analisis Pengaruh Biaya Peralihan (Switching Cost) terhadap Loyalitas Pelanggan Operator Seluler Dari GSM (Global System For Mobile Communcations) Ke CDMA (Code Division Multiple Access) di Kota Medan
Bibliographic record
Abstract
Loyalty is a comitment of customer to rebuy the product and continiously consistent to use the product or service in the future. Otherwise switching cost define as a barrier that lock in the customer to switch their seller for a new seller. Celluler Customer is increasing rapidly in this decade. Therefore the celluler provider is ask to increase its number of customers and keep their existing costumer. The customer of GSM is grow highly otherwise the customer of CDMA is grow slowly. Decision to buy and continiously use the celluler is depend on many factors. This research is conduct to explain switching cost factors influence in costumer loyalities of celluler operator from GSM to CDMA in Medan. Switching cost factors consist of three variables those are Procedural cost, Relational Cost, and Financial Cost. This research is explanatory with primary data which is collecting by using questionnaires .survey Population and sample of this research are 387 responden of cellular customer in Medan. By using correlation and multiple regression analysis this research found that switching cost influences significantly and positively to customer loyalites of celluler operator from GSM to CDMA in Medan. Keyword : Switching, procedural, financial, relational, loyality Rusdi Saleh Siregar : Analisis Pengaruh Biaya Peralihan (Switching Cost) Terhadap Loyalitas Pelanggan Operator Seluler Dari GSM (Global System For Mobile Communcations) Ke CDMA (Code Division Multiple Access) Di Kota Medan, 2009 USU Repository © 2008
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.019 | 0.002 |
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".