Frontline service employee research: integration of systematic literature reviews and recommendations for future scholarship
Bibliographic record
Abstract
Purpose This paper integrates the findings of the articles included in the special issue (SI) on frontline employee (FLE) research. Articles included in this SI systematically review multiple research domains, including employee and customer engagement, FLE vulnerability, customer mistreatment, service teamwork and service encounters; provide instructions on effectively conducting meta-analyses and discuss the practical applications of FLE research. This paper also provides future directions for FLE scholarship with a focus on theoretical/methodological rigor and relevance. Design/methodology/approach This is a conceptual paper that integrates and critically evaluates extant research and provides directions for future scholarship. Findings An integrative framework of extant FLE research is proposed consisting of situational predictors, psychological mechanisms, attitudinal/behavioral outcomes and boundary conditions/moderators. Further, three main areas for future scholarship are recommended including examining the transformative effects of technology on FLE work, focusing on decent work for FLEs and conducting practically relevant and impactful research. Originality/value This paper provides reflections, integration and future directions for scholarship based on systematic reviews of key domains of FLE research, a primer for conducting systematic reviews (specifically – meta-analysis) and practitioner perspectives on extant research.
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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 | 0.238 | 0.487 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.043 | 0.035 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".