Investigating different factors influencing on customer retention in e-business
Bibliographic record
Abstract
This paper presents an empirical investigation to study the impact of brand value, service quality, customer trust and internet knowledge on customer retention.The study adoptes a questionnaire originally developed by Jurisic andAzevedo (2011) [Jurisic, B., &Azevedo, A. (2011).Building customer-brand relationships in the mobile communications market: The role of brand tribalism and brand reputation.Journal of Brand Management, 18(4), 349-366.]and distributes it among some people who purchased LED television from a well-known Korean manufacturer in city of Tehran, Iran.Using structural equation modeling, the study has determined positive effects of brand value (β = 0.377, Sig.= 0.0001), service quality (β = 0.204, Sig.= 0.0001) and internet knowledge (β = 0.280, Sig.= 0.0001).However, the study does not find any statistical evidence to confirm the effects of customer trust on customer retention.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".