The effect of customer orientation on frontline employees job outcomes in a new public management context
Bibliographic record
Abstract
Purpose This paper seeks to examine the role of customer orientation in a model of affective and behavioural job outcomes grounded in Bagozzi's reformulation of attitude theory in the new public management context of a former public sector government department that has undergone corporatisation and now operates as a state‐owned enterprise (SOE). Design/methodology/approach Frontline employees (FLEs) complete a self‐administered questionnaire on how customer orientation affects their job satisfaction and organisational commitment, and how these job attitudes impact service recovery performance and turnover intentions. Data obtained from the FLEs were analysed using the structural equation modeling‐based partial least squares methodology. Findings Seven of eight hypotheses are supported. Results suggest that there is a significant influence of customer orientation on job satisfaction and organisational commitment, which in turn influence service recovery performance and turnover intentions. Practical implications The research advances understanding of the influence of customer orientation on affective and behavioural job outcomes. SOE managers can take actions on a number of different fronts to assist progress towards improving FLE service recovery efforts and reduce turnover intentions. Originality/value The impact of customer orientation on affective job outcomes (job satisfaction and organisational commitment) and behavioural job outcomes (service recovery performance and turnover intentions) has not been investigated in the context of SOEs.
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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 | 0.002 | 0.004 |
| 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 | 0.002 | 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".