Corporate entrepreneurship, supply chain management strategies and performance in the setting of Saudi Arabia: Empirical investigation
Bibliographic record
Abstract
The objective of this study is to assess the joint effect of corporate entrepreneurship (CE) and supply chain management (SCM) strategies on firm performance in the circumstance of Saudi Arabian manufacturing firms. This is interesting in the sense that while previous scholars have focused on CE and SCM separately, the combinative aspect of the two particularly in nonwestern countries seems still to be short of attention. In this respect, this paper attempts to apply a resource-based view and suggest that CE and SCM complement each other and in the end performance of the firm will be increased. Out of the 84 Saudi manufacturing firms covered in the structured survey, data was analyzed using Ordinary Least Squares (OLS) regression. The results indicate that CE, particularly dimensions such as new business venturing, product innovation, technological entrepreneurship, mission reformulation, reorganization and system-wide changes, significantly enhances firm performance. Additionally, SCM strategies were found to have a positive and statistically significant impact on firm performance, highlighting the critical role of effective supply chain management in operational efficiency and competitive positioning. The findings contribute to the literature by providing empirical evidence from a Middle Eastern context, thereby enhancing the generalizability of existing theories on CE and SCM. The study also offers practical implications for managers and policymakers, suggesting that aligning CE initiatives with SCM strategies can drive sustained competitive advantage. These insights are particularly relevant for firms operating in emerging markets, where economic reforms and diversification efforts, such as Saudi Arabia's Vision 2030, necessitate strategic innovation and supply chain optimization.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".