Exploring the relationships of strategic entrepreneurship and social capital to sustainable supply chain management and organizational performance
Bibliographic record
Abstract
Purpose The purpose of this paper is to draw upon the resource-based view (RBV) of the firm in an attempt to explore how a firm’s resources (i.e. assets and capabilities) such as social capital (SC) and strategic entrepreneurship (SE) relate to sustainable supply chain management (SSCM) and organizational performance (OP). Design/methodology/approach Data were collected by questionnaire survey from the supply chain and logistics managers of 242 manufacturing firms in Pakistan. The structural equation modeling approach was used to test the hypotheses. Findings The results provide support for the proposed hypotheses. The results indicate that SC and SE are positively related to OP. However, the findings show a positive but weak association of SC and SE with SSCM. In a developing country context of Pakistan, organizations are more likely to employ SC and SE for achieving OP. However, relatively less emphasis is placed on linking SC and SE to SSCM. Pakistani organizations need to integrate SSCM into their business strategies. It is concluded that organizations in Pakistan though have some degree of involvement in SSCM but still face some challenges. Originality/value The current study attempts to narrow the gap in the available literature in three important aspects. First, it makes the contribution to the literature on SSCM by employing RBV and exploring the relationships of a firm’s resources (i.e. SC) and capabilities (i.e. SE) to SSCM and OP. Second, it employs a relatively more comprehensive measure of SE compared to the limited measures in existing empirical research. Third, the examination of the links of SE and SC to SSCM and OP is of particular importance in the context of a developing country such as Pakistan.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".