Service customer commitment and response
Bibliographic record
Abstract
Purpose This paper aims to examine the role of three forms of customer commitment (normative, affective, and continuance) on a variety of loyalty‐related customer responses. Design/methodology/approach Data were collected from two distinct sampling frames, which yielded a combined metrically invariant sample of 348 consumers. A three‐dimensional conceptualization of commitment is used to analyze impacts on one focal (i.e. repurchase intentions) and two discretionary customer responses. Findings Results of structural equation modeling analyses indicate that affective commitment is the primary driver of the customer responses and mediates the effects of normative and continuance commitments. These effects are contingent upon the type of service. Research limitation/implications This research emphasizes the primacy of affective commitment in predicting loyalty‐like customer responses. Practical implications Managers need to focus primarily on generating affective commitment, but be mindful that normative and continuance commitment also play a role in generating desirable consumer responses. Originality/value The paper builds on and overcomes several deficiencies in prior commitment research. A more accurate and useful representation of affective, normative, and continuance commitment roles in generating focal and discretionary behaviors is provided.
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".