Frontline employee competencies for technologically complex service environments: a conceptual model of mindfulness orientation
Bibliographic record
Abstract
Purpose Technological innovations are rapidly transforming service frontlines, resulting in increasingly complex service touchpoints. These touchpoints place greater demands on frontline employees (FLEs) to deliver a positive customer experience. Despite the considerable extant body of knowledge on FLE competencies, the literature on frameworks for managing the complexity of contemporary frontlines from the FLE’s perspective is sparse. This paper aims to fill this critical gap by developing a framework that enables FLEs to deliver positive moments of truth (MOTs) while ensuring the well-being of all actors involved. Design/methodology/approach This paper uses a conceptual approach rooted in the organizational mindfulness and individual mindfulness literature as the theoretical lens. This is complemented by a comprehensive review of the FLE skills literature supported by marketplace examples to illustrate the optimal use of the said skills. Findings This paper proposes a conceptual framework of mindfulness orientation which delineates how FLE competencies underpinned by a set of key skills can deliver positive MOTs and actor well-being. Research limitations/implications The research is conceptual in nature and does not contain validation through empirical data. Practical implications This comprehensive skill set provides a clear roadmap for firms in both recruitment and developing training for their FLEs, thus contributing to practice. Originality/value Firstly, we present a conceptual framework of mindfulness, combining organizational mindfulness and individual mindfulness that will enable employees to help facilitate the creation of positive MOTs. Secondly, we develop a comprehensive set of employee skills that underpin the mindfulness orientation framework.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".