Bibliographic record
Abstract
Purpose – The authors aim to study the direct and moderating effects of relationship age, continuance commitment and satisfaction on the generation of positive and negative word of mouth (P/NWOM). Design/methodology/approach – Hypotheses based on the notion of liability of adolescence and the motivation to generate P/NWOM were tested with data collected through a survey of a random sample of customers of fixed-line telephone users. Findings – Relationship age adversely impacts PWOM and the effect of satisfaction on both P/NWOM. Continuance commitment increases NWOM and causes dissatisfied customers to generate greater NWOM while not affecting the PWOM of satisfied customers. Satisfaction shows a significant non-linear effect on WOM. Research limitations/implications – Future research could conduct longitudinal or experimental work to explicate the causal mechanisms underlying these cross-sectional survey results. Research could also extend these results to a B-B context. Practical implications – Results offer strong evidence of a dark side to long-term customer relationships. Recommendations focus on managing long-term relationships and perceptions of continuance commitment to minimise adverse effects. Originality/value – As far as the authors know, this research is the first to offer a theoretically grounded explanation of the direct and moderating effects of relationship age on P/NWOM behaviour. Results challenge the premise of long-term customers being a panacea for numerous problems faced by firms. Findings also help explain the contradictory results in prior research on the effects of continuous commitment on WOM.
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.
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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.009 |
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".