Evaluating the impact of project procurement strategies on supply chain efficiency: The role of vendor relationship management as a mediator variable
Bibliographic record
Abstract
This study evaluates the impact of project procurement strategies on supply chain efficiency in the Jordanian construction industry, with vendor relationship management as a mediating variable. The target population was the 25000 construction industry specialists in Jordan. According to purposeful sampling technique and more specifically, stratified random sampling, 454 subjects were asked to complete questionnaires. Partial least squares structural equation modeling was employed for the analysis of data for the test of the hypotheses. This research also revealed that the project procurement strategies which are Just-In-Time, Multiple Sourcing, and Single Sourcing have positive impacts with a statistical significance to the supply chain efficiency dimensions of Demand Forecasting, Supply Chain Integration and Collaboration and Communication. Vendor relationship management was also a partial mediator to these relations. These implications can be summed up for the construction firms and policymakers in Jordan to understand how strategic procurement approach and proper relations with the vendors can improve the performance of the supply chain. This work aims to benefit the academia as it fills a gap in the existing literature by analysing the relationship between procurement strategies, its actors (vendors), and supply chain performance within a developing country environment. The analysis with the help of PLS-SEM enables us to look at both the direct and the indirect effects in such relationships.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".