The impact of sourcing strategies and logistics capabilities on organizational performance during the COVID-19 pandemic: Evidence from Jordanian pharmaceutical industries
Bibliographic record
Abstract
Achieving and maintaining good business performance is a core concern of every business entity. This quantitative study investigates the impact of sourcing strategies and logistics capabilities on the performance of Jordanian pharmaceutical enterprises using partial least square structural equation modeling (PLS-SEM). The views and perceptions of 951 managers and assistant manager respondents working in Jordanian pharmaceutical companies were gathered anonymously via an electronic online questionnaire using a convenience sampling technique. The findings revealed that sourcing strategies and logistical capabilities have a significant positive impact on organizational performance in pharmaceutical companies. Insourcing, near-sourcing, few / many suppliers, joint ventures, and virtual enterprises were perceived to be effective sourcing strategies in improving organizational performance. In contrast, outsourcing, and vertical integration were perceived to have a negligible impact on the performance in the context of pharmaceuticals. Furthermore, the findings confirmed that individual logistic capabilities (safety and compliance, storage, delivery, and imports and exports) of pharmaceutical firms were perceived as impacting positively on firm performance. This research provides useful insight for decision makers in pharmaceutical companies in Jordan when reviewing their supply chain, particularly during challenging and turbulent times such as the COVID-19 pandemic.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".