Impact of Live Chat Service Quality on Behavioral Intentions and Relationship Quality: A Meta-Analysis
Bibliographic record
Abstract
The purpose of this research is to synthesize by means of a meta-analysis knowledge acquired to date on the impact of live chat service quality on customer behavioral intentions and relationship quality. An analysis of twenty-nine topical studies demonstrates that live chat capability unquestionably contributes to the formation of customer behavioral intentions, while enhancing customer relationship quality. The study also identifies those variables which exert a moderating effect on behavioral intentions and relationship quality (e.g., age, culture, product typology), and therefore sheds light on the conditions in which these services generate the most benefits. Based on the findings of the meta-analysis, the authors point to gaps in the literature, propose a varied choice of topics for future research by academics, and suggest an extensive lineup of tools and advice for managers.
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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 | 0.055 | 0.118 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.039 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".