Predictors of Complain Behaviour among Mobile Telecommunication Network Consumers
Bibliographic record
Abstract
This research paper sought to empirically determine the predictors of consumer complain behaviour. High prevalence of anomalies with mobile telecommunication network services without a lasting solution emphasized the significance of the present study. Hawkins, Best, & Coney (2004) consumer complain behavioural framework was used as the study’s theoretical background. It was a quantitative study which employed a cross-sectional survey. A total of 385 respondents within Festac town in Nigeria were selected stratifiedly for the study. Descriptive analysis showed that 64.9% of the respondents are passive while 35.1% are active complainers. Regression analyses revealed that six demographic variables explained 4.9% variance on complain behaviour while dissatisfaction explained additional 10% variance on complain behaviour. It further indicated that three control measures were significant, with dissatisfaction (β=-.33, p <.001), religion (β=.12, p <.05) and level of income (β=.11, p <.05). Dissatisfaction is an insubvertable factor of complain behaviour regardless of consumers’ religious affiliation and level of income. Firms should try to alleviate dissatisfaction, encourage active complain, and pay more attention to consumers’ religiosity and level of income since these variables influence complain behaviour.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".