Effect of Refresher and Skill Transfer Initiatives (RSTI) to BHP Representatives’ Sales Stats Performance
Bibliographic record
Abstract
This study investigates the impact of Refresher and Skill Transfer Initiatives (RSTI) on the sales performance of Bank of Montreal Home Advantage Plus (BHP) sales representatives. The research aimed to determine whether RSTI contributes to enhancing the sales performance of BHP representatives. Employing a mixed-methods approach, the study selected 17 employees through purposive sampling and collected data using various instruments including Participant Observation, Sales Reports, Focus Group Discussions, and Feedback Forms. Both quantitative data, analyzed through descriptive statistics, and qualitative data, examined thematically, informed the analysis. The study utilized Jamovi Analysis to derive average mean scores and interpret the statistical significance of RSTI’s impact post-implementation. The findings highlight the positive effect of RSTI on BHP sales representatives, noting significant improvements in sales and behavioral performance. Feedback from participants further endorsed RSTI, advocating for its continued use. This research enriches Sales Training literature by introducing behavioral performance enhancements, such as compliance, selling, and soft skills, as critical outcomes of RSTI. The results affirm that RSTI significantly bolsters sales personnel’ sales performance and skill sets. Keywords: Refresher and Skill Transfer Initiatives, sales training, skill transfer, soft skills, behavioral performance
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".