Service customer commitment and response
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it