Classifying Variables with Cluster Analysis when Measuring Quality of Services in Contact Centers
Bibliographic record
Abstract
The goal of the paper was to discover, whether using one of multidimensional exploratory techniques cluster analysis in quantifying quality of services in contact centers brings logical classification of variables and if this classification can be used and measure quality of these services. On a basis of literature studies important attributes of services delivered by contact centers were identified. They were examined as observable variables with the use of a computer assisted telephone interview method on a sample of 1000 contact center customers. Then, variables were classified using cluster analysis. Clusters link observed data into meaningful structures, that is, develop taxonomies. Using factor analysis to quantify and measure quality of contact centers allowed to distinguish the following clusters: „answer”, „empathy”, „availability” and „time”. The profile of contact center services quality obtained from cluster analysis shows that the highest quality assessment is for the cluster „answer”, then for „availability”. The quality concerning cluster „empathy” is visibly lower, while the cluster „time” is of decidedly the lowest quality assessment. Proposition of classifying variables into clusters creates a theoretical model which quantify quality of services delivered by contact centers and make its structure more comprehensible. In practice, a proposed classification allows to identify quality gaps and design contact centers services with a special attention paid to the matters of quality to meet customers’ expectations. The paper’s contribution is a novel way of quantifying and measuring quality of services in contact centers. JEL Classification Code: C380, M310 .
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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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".