A Structural Equation Modelling Evaluation of Antecedents and Interconnections of Call Centre Agents’ Intention to Quit
Bibliographic record
Abstract
Call centers play a significant role in the operational dynamics of different types of businesses. This is especially the case because a call center agent’s demeanor can impair or engender customer satisfaction, which has ramifications for business patronage. Unfortunately, the pressures associated with the role of the call center agent have made staff attrition a norm in the industry. While this does not augur well for the call center or the organizations that they serve, the role of possible antecedents in the equation of staff attrition in South African call centers remains largely unexplored. Using a structural equation modeling approach, this study examined the interconnections between customer orientation, knowledge management, job satisfaction, and employees’ intention to quit. Additionally, the mediating influence of job satisfaction on the association between customer orientation and knowledge management of the intention to quit is examined. This study found significant relationships between knowledge management, customer orientation, and job satisfaction and the dependent variable (intention to quit). In addition, this study establishes that the extent to which job satisfaction may mediate the influence on the intention to quit hinges on the organizational element considered. Two factors limit the extent to which the findings from this study can be generalized. First, this study focused on the call center setting in South Africa. Second, convenience sampling was used in this study. This study points to critical operational practices that call center managers can embrace toward enhancing job satisfaction and reducing intention to quit propensity. Using structural equation analysis, we contend that call centers in the South African setting would effectively address staff attrition if appropriate organizational practices are endorsed toward ensuring employee job satisfaction.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".