Smooth operators? Drivers of customer satisfaction and switching behavior in virtual and traditional mobile services
Bibliographic record
Abstract
The present study analyses the creation of customer satisfaction and loyalty, along with the influence of switching costs in the mobile services’ market, by analyzing network mobile services – the so-called traditional operators – and virtual mobile services, in order to empirically and conceptually investigate the difference between these mobile services’ operators. A conceptual model is tested by developing structural equation modeling , in the context of a European mature market – the Spanish market, gathering a sample of 524 mobile services’ users. The analysis highlights that mobile service value exerts the strongest influence on customer satisfaction for both type of mobile companies, while the attractiveness of alternatives is the more relevant switching costs; despite some interesting differences were found between the virtual and the traditional companies. Considering our findings mobile companies should seek to improve their customers’ perceptions of the core services offered, stressing the importance of the prices charged for the services and the functional benefits provided. Moreover, we suggest to personalize mobile services in order to provide with higher value to customers, since value-added services make consumers more satisfied.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 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".