Digital entrepreneurship and its impact on digital supply chains: The mediating role of business intelligence applications
Bibliographic record
Abstract
This study aimed to test and evaluate the impact of Digital Entrepreneurship and its impact on digital supply chains in Jordanian hotels, and the mediating role of digital supply chains in this relationship. The descriptive analytical method was used, and the study population consisted of (835) male and female employees, and a random sample was used with a simple random sample of (342) participants. To achieve the objectives of the study, a developed questionnaire was used to collect data from the sample members. The study adopted the Statistical Package for Social Sciences (SPSS.V.22) and Structural Equations Modeling (SEM) using the AMOS program for path analysis and to perform statistical analysis using: Descriptive and inferential statistics measures, including Multiple linear regression, Pearson correlation coefficient, skew coefficient, multiple linear correlation, variance inflation coefficient and permissible variance. The study reached results, the most important of which were: The most important results of the study were: the presence of an important impact of Digital Entrepreneurship in digital supply chains and the presence of a significant impact of Digital Entrepreneurship through business intelligence applications as an intermediate variable in digital supply chains. The study recommends the need to enhance Digital Entrepreneurship in Jordanian hotels by focusing on the holistic view of these hotels and their environment, whether the internal environment that focuses on strengths and weaknesses in the hotel’s capabilities or their external environment that brings opportunities and challenges.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".