Child helplines: how social support and controllability influence service quality and well-being
Bibliographic record
Abstract
Purpose The authors investigate how employee social support impacts children’s perceptions of service quality of a child helpline chat service and the chatters’ immediate well-being. Specifically, the purpose of this study is to examine how action-facilitating support, nurturant support and emotional reflections influence the children and to test whether this impact varies depending upon the controllability of the issues discussed. Design/methodology/approach The authors develop hypotheses about the influence of social support and controllability on children’s perceived service quality and well-being. Chat conversations are coded on the social support given by the employee and the controllability of the issue. Questionnaires are collected to measure children’s service quality and well-being. Using structural equation modeling, hypotheses are tested with a sample of 662 children and chat conversations of a child helpline. Findings The study reveals that for children chatting about controllable issues, nurturant support and negative emotional reflections negatively influence the immediate well-being of these children. Positive emotional reflections positively influence immediate well-being. For children chatting about uncontrollable issues, nurturant support and negative emotional responses positively influence the perceived service quality. Originality/value This study contributes to the services marketing literature by broadening the current understanding of the impact of social support on children’s service quality perceptions and well-being, and by showing how this impact is moderated by the level of controllability of the issue discussed.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".