EXPECTANCY AND SALES PERSONNEL MOTIVATION TO CONTINUE WORK PERFORMANCE DURING THE COVID 19 PANDEMIC
Bibliographic record
Abstract
Declining level of public purchasing power affects several industries during the COVID- 19 pandemic. As one of the industries affected by the COVID-19, the decrease in sales performance felt by one of the telecommunication providers in Bali, Indonesia which has incurred loss in the first quarter of 2020. As a result, the sales personnel have experienced low level of motivation. The purpose of this study was to investigate howto motivate the sales personnel to continue work performance during the COVID-19 pandemic under the expectancy theory approach that consists of expectancy, instrumentality, and valence. The theory proposed if high level of efforts will lead to the attainment of high level of performances and high level of performances will be valued with desired outcomes in the future. Convenience sampling was used in this study. 110 respondents were collected and analyzed. The result shows simultaneous influences of independent variables toward dependent variable at p< .001 as the level of significance and the values of F= 69.493. This indicates that the sales personnel believe if theamount of effort given will be associated with the achievement of better performance and rewards. This study highlights the presence of expectancy variable which has a positive but not significant influence on motivation. This study suggests that expectancy variable should be increased while maintaining instrumentality and valence by setting realistic and achievable targets during the COVID-19 situation, providing necessary equipment and facilities to maximize skills and resources. Keywords: Expectancy, Instrumentality, Valence, Motivation, Sales personnel, Telecommunication, Indonesia
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".