Factors Influencing Employee Performance on Flexible Working for a Sales Function within an Organisation
Bibliographic record
Abstract
This research paper aims at identifying employee performance factors for a sales function within an organisation looking to adapt a flexible working model. With the rise of demand of flexible working during COVID 19, we have seen that some organisations have already started working on implementing this approach. However, some organisations believe it’s important but have yet to roll this model out to their employees. As the approach to flexible working has become a wide topic, several studies have attempted to measure the impact on flexible working within an organisation with limited studies on how flexible working effects a certain function within an organisation. This study uses a qualitative approach to identify employee performance factors needed within a sales function to help measure performance while adapting a flexible working model. Although the original plan was to conduct 10 interviews, participants availability was limited since interviews were done during quarter three which is considered summer vacation for the sales population. Findings suggest that employee satisfaction and wellness align with other definitions in different studies. However, factors within employee performance are different as managers suggest learning, coaching and sales rigor to be key performance indicators for employees looking to avail for a flexible working model. This study will act as a starting framework for academics who are looking to deep dive in identifying flexible working models for certain functions within an organisation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 teacher head, 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".