An Empirical Study of Service Innovation's Effect on Customers' Re-Purchase Intention in Telecommunication Industry/ UNE ÉTUDE EMPIRIQUE DES EFFETS DE L'INNOVATION DES SERVICES SUR L'INTENTION DE RACHAT DES CONSOMMATEURS DANS L'INDUSTRIE DE TÉLÉCOMMUNICATION
Bibliographic record
Abstract
Based on the review of the relevant researches in the field of repurchase intention as well as the field of service innovation, this research analyzed the Service Innovation’s Effect on Customers’ Re-purchase Intention in Telecommunication Industry, and developed the Theoretical model. And 198 users of mobile telecommunication were chosen for our investigation. The result shows that service innovation made by the operators in telecommunication industry has important effect to users’ intention when they are in need to repurchase the mobile telecommunication. Key words: service innovation; repurchase intention; telecommunication industryResume: Sur la base des etudes appropriees dans le domaine de l'intention de rachat, et dans le domaine de l'innovation des services, cette recherche a analyse l'effet de l'innovation des services sur l'intention de rachat des consommateurs dans l'industrie de telecommunication, et a developpe un modele theorique. 198 utilisateurs de telecommunication mobile ont ete choisis pour notre enquete. Le resultat montre que l'innovation des services effectuees par les operateurs dans l'industrie de telecommunication a un effet important sur l'intention des consommateurs quand ils sont dans le besoin de racheter la telecommunication mobile.Mots-cles: innovation des services, intention de rachat; industrie de telecommunication
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".