Unintended consequence in implementation of work culture improvement program through peer-coaching in a sales and distribution center of a large multi-national high technology company
Bibliographic record
Abstract
The purpose of this paper is to analyze the work culture improvement program (WCIP) by utilizing the peer-coaching method among sales supervisors in Sales and Distribution Center at a Large Multi-National High Technology company. The present study also investigates unintended consequences longitudinally using two stages of a qualitative approach. First, the WCIP, initiated and supported by senior management, was delivered by a Canadian-based consulting team was discussed as a case study. Next, an interview was administered among nineteen respondents with the use of ORID (objective questioning, reflective questioning, interpretative questioning, and decision-oriented questioning) framework. The R Statistics RQDA package analyzed the WCIP's impact using the Echo method of interview. Findings revealed aside from the improvements in interactions, the peer-coaching circle has turned the peer-coaching circle into a social group, an unexpected beneficial result. Furthermore, the nature of the work of the group, being in Sales and Distributions, forced them to a network to gather additional help and information. It appears that the result of the peercoaching approach might be a function of the following factors: questioning methodology, nature of work, the frequency of meeting, and management support.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".