The influence of satisfaction, trust and switching barriers on customer retention in a continuous purchasing setting
Bibliographic record
Abstract
Adopts a holistic approach that examines the combined effects of satisfaction, trust and switching barriers on customer retention in a continuous purchasing setting. Argues that such an approach helps uncover hitherto neglected effects on retention and, in the process, unveils more cost effective ways of retaining customers. Drawing on this framework develops several hypotheses regarding the main and interaction effects of customer satisfaction, trust and switching barriers on retention. Tests these hypotheses on data from a large‐scale mail survey of fixed line telephone users in the UK, finding that both customer satisfaction and trust have strong positive effects on customer retention. Contrary to some assertions in the literature, however, finds that the effect of trust on retention is weaker than that of satisfaction. Nevertheless, the interaction between trust and satisfaction also has a significant effect on retention, indicating that building both customer satisfaction and trust is a superior strategy to a focus on satisfaction alone. Qualitative evidence from the survey offers further support for this finding. Even a “satisfying” service recovery process might be inadequate to prevent loss of trust, with significant implications for future consumer behaviour. Finally, the results show that switching barriers have both a significant positive effect on customer retention as well as a moderating effect on the relationship between satisfaction and retention. While service providers may be able to retain even dissatisfied customers who perceive high switching barriers, argues that ideally, firms should aim at a combined strategy that makes switching barriers act as a complement to satisfaction.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".