Determinants of operational performance of pharmaceutical wholesalers’ companies in Bali province
Bibliographic record
Abstract
Pharmaceutical distribution companies or also known as Pharmaceutical Drug Wholesalers (PBF) play a significant role in the distribution of pharmaceutical products in Indonesia. Without the role of PBF, the drug will not reach the patients from the manufacturer. The PBF appointed by the manufacturer to distribute the product, has operational performance targets that must be achieved. However, like other businesses, PBF operational performance is also affected by external factors, such as the COVID-19 pandemic, government regulations and competition intensity. The impact of internal factors such as limited company resources is also investigated in this research. This research tries to explore the impact of external factors, those are: the pandemic of COVID-19, government regulations, competition intensity and internal factors that are the company's limited resources to PBF operational performance. Sales strategy is also used as mediating variables from external and internal factors to operational performance, thus operational control as moderating variable from sales strategy to operational performance respectively. Data collection was obtained by questionnaires and interviews with 44 PBF operational leaders in Bali, Indonesia. Data analyzing using SmartPLS 3.0. The COVID-19 pandemic, government regulations, competition intensity and company’s limited resources have a negative impact on the company's operational performance. Sales strategy serves as a mediating variable from external and internal factors to operational performance. Operational control plays a significant role in moderating sales strategy and the performance of operational. The theoretical implication of this research is to enhance The Contingency Theory, which claims that the business environment, strategy, and control are three major contingency elements that are linked. The findings of this study also strengthen the concept of Transaction Cost Theory, which states that the relationship between the parties in a transaction is associated with rights and obligations that are poured into a very detailed agreement. The operational performance required by the principal to PBF is also included in the agreement and PBF should strive to achieve that operational 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".