Assessing supply chain performance through the interplay among success drivers
Bibliographic record
Abstract
This study examines the success drivers of Supply Chain Performance (SCP). This study aims to examine the interrelationships among various proposed success drivers of SCP that were analyzed individually or collectively in some previous studies. Four proposed forces have been identified in this study including organizational culture, SC relationships, SC integration, and SC innovation. SC integration is seen as a mediating factor between SC relationships and SCP. Using a deductive and quantitative approach that is based on collecting data from (17) companies in the field of logistics using an online survey, the study focuses on four success drivers including organizational culture, SC innovation, SC relationships, and SC integration. Smart PLS 3 software was applied to analyze the data based on the use of Structural Equation Modelling (SEM). The findings confirm that organizational culture and SC innovation have a direct significant impact on SCP. Regarding the mediating role of SC integration, the finds confirmed that SC integration mediates the relationship between SC relationships and SCP. A set of implications and recommendations for decision-makers and researchers are proposed based on the empirical findings of the current study. SC managers must first consider organizational culture and try to create positive supportive cultural attributes. A culture that encourages openness to change, sharing of knowledge, and collaboration seems a necessity to further improve SCP. Additionally, as the study findings emphasized the significant impact of SC innovation, SC managers should encourage innovative initiatives and behavior.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".