The strategic impact of technology based CRM on call centers' performance
Bibliographic record
Abstract
The primary objective of this paper is to test a model that can explain the impact of technology based CRM on inbound call center performance. To do this, data were collected from 168 call center managers and analyzed through structural equation modeling. The research findings indicate that technology based CRM significantly affects first call resolution and perceived service quality, but weakly influence caller satisfactions through the mediating role of first call resolutions.Observably, this research believes that customer contact centers as the first touch points to company are dependent on other factors such as company policy, product quality, customer characteristics, etc. to influence caller satisfactions, but unfortunately most of these factors fall outside the operational control of contact center activities. The findings in this research has empirically provided the long waiting evidence that technology based CRM applications within the inbound contact center industry can only influence caller satisfactions through first call resolution and perceived service quality. A major implication for call center managers is that this research findings has availed them the opportunity on how to effectively develop, implement, and evaluate their CRM applications.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".