The moderating effect of normative commitment on the service quality-customer retention relationship
Bibliographic record
Abstract
Purpose – Limited attention has been given to the effects of normative commitment (NC) in a marketing relationship. This paper investigates the effects of service quality and normative commitment on customer retention in a consumer-retailer relationship. Design/methodology/approach – Two distinct studies; a longitudinal experiment and a SEM model were conducted to tease out the normative commitment-service quality interaction on customer switching intentions in services. Findings – Both studies supported the existence of a significant normative commitment-service quality interaction on switching, in addition to the main effects of both variables. Research limitations/implications – The longitudinal experiment has the limitation of being a simple test of theory in a controlled setting. Study II validates this theory in a real-world retail services setting, but there are questions about the extent to which the relationship may hold in other service sectors. The results indicate that the effect of service quality on customer loyalty is moderated by normative commitment. This may also allow us to think about customer commitment in a new way in that it could be a construct rooted in attitude confidence rather than attitude. Practical implications – The findings allow practitioners to recognize that the development of obligation-based normative commitment can give them a basis for successful competition against other firms, even those that may outperform them on other salient attributes, including basic service quality. Originality/value – This is one of a very small number of studies in the discipline that have examined the effects of normative commitment and the first that has demonstrated that normative commitment moderates the service quality-service customer retention relationship. This opens the door for the possibility that other forms of commitment may moderate the relationship between service quality and 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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".