A Cross-national Investigation of the Satisfaction and Loyalty Linkage for Mobile Telecommunications Services across Eight Countries
Bibliographic record
Abstract
Improving customer satisfaction has become a strategic imperative for managers and researchers given the benefits of developing customer loyalty for long-term financial success. Creating these linkages becomes even more important in the context of mobile telecommunications due to the ubiquitous nature of mobile phones and the potential this creates to engage in interactive marketing for firms. Further, with increased global penetration of mobile telecommunications, examining cross-national differences in consumer attitudes and behaviors has become critical. Most studies that examine customer satisfaction and loyalty linkages however have traditionally focused on single countries and/or single industries. This study extends the literature by testing the moderating impact of cultural variables on the impact of satisfaction on loyalty intentions using data from 3,393 mobile telecommunications customers in Australia, Brazil, Canada, China, France, Spain, UK, and USA. Our findings reveal that the impact of satisfaction on loyalty in the mobile telecommunications context depends on cultural differences. The results demonstrate non-linear threshold effects where managers operating in countries characterized by self-expressionist values will have an easier time creating satisfaction and loyalty with mobile customers compared to those operating in cultures dominated by high survivalist values.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".