A Study on Strategic Entrepreneurship Orientations: Indicators, Differential Pathways, and Multiple Business Sustainability Outcomes
Bibliographic record
Abstract
Awareness of strategic entrepreneurial orientations (SEO) has grown in sustainability research in small and medium-sized businesses (SMEs), and the extant literature includes review studies concerning SEO.Despite the existence of these studies, in the Jordanian context, more research is needed about a parsimonious model that includes the underlying mechanisms linking SEO to multiple sustainability performances.Given such a gap, this study proposes an updated conceptual model of SEO using the Resource-Based View theory (RBV) and Social Capital Theory (SCT) as the theoretical underpinnings.Specifically, this research links SEO to Jordanian SMEs sustainability performance, like economic, environmental, and social practices, through the mediating roles of sustainable supply chain management (SSCM).In addition, a relevant literature search highlights three indicators of SEO: innovativeness, risktaking, and proactiveness.This reviewed conceptual framework was created through a process of qualitative analysis including a synthesis of the literature, analysis, and integration with existing models.A conceptual model has an essential role in the scientific processes.Its purpose is to aggregate evidence, help in understanding phenomena, inform future studies and act as a reference operational index in practical settings.The study also creates avenues for future research and provides theoretical contributions.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".