Pengaruh Motivasi, Pelatihan dan Lingkungan Kerja Non Fisik Terhadap Kinerja Sales People pada Jaringan Dealer PT. Astra Honda Motor di Provinsi Nusa Tenggara Barat
Bibliographic record
Abstract
Indonesia's economic growth is supported by several sectors, one of which is the manufacturing sector. The manufacturing sector contributed 16.77 percent to national economic growth in the first quarter of 2023. According to the 2022 business competition index in Indonesia, Indonesia's score is 4.87 on a scale of 7, which has increased from previous years. The increase in the business competition index number illustrates the company's need to be able to optimize its resources and increase company productivity. In a company, employee performance greatly affects the quality of the products and services produced. With the need to increase company productivity, employee performance improvement is also needed so that companies are able to maintain business existence and encourage better company progress. Companies that are able to encourage the performance of their employees tend to have better quality products and services. Therefore, companies must encourage the performance of their employees in order to compete with changing market conditions from time to time. This study aims to determine the effect of motivation, training and non-physical work environment on the performance of sales people at the dealer network of PT. Astra Honda Motor in the province of West Nusa Tenggara (NTB). This research is a quantitative research with a sample of 90 sales people, the data collection method was carried out using an online questionnaire with a Google form. The analysis technique used in this study is multiple linear regression with hypothesis testing t-test and F-test. The results showed that motivation, training and the non-physical work environment had a partial and simultaneous positive effect on the performance of sales people at the dealer network of PT. Astra Honda Motor in the province of NTB.
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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".