Using cloud computing services to enhance competitive advantage of commercial organizations
Bibliographic record
Abstract
Using advanced technology in business has created hyper-competition among organizations to satisfy customers' needs. Using advanced technology aims to provide customers with quality products/services at suitable prices in the right place better than competitors. Therefore, the current study's purpose is to explore the influence of cloud computing services on Jordanian commercial organizations’ competitive advantages, organizations which use cloud computing services. The study uses quantitative, cause-effect, and cross-sectional methods and uses a convenience sampling approach to collect the data by questionnaire from 111 managers and/or owners of commercial organizations. The collected questionnaires are examined and inserted into SPSS. The instrument validity, normal distribution, and reliability are verified, then descriptive analysis is performed, the relationship between independent and dependent variables is tested, and finally multiple regressions are used to test the hypotheses. The findings indicate that commercial organizations are concerned about cloud computing services as well as competitive advantage sub-variables. The results also show that there was a significantly strong correlation between cloud computing services and competitive advantage. Moreover, cloud computing services influence the dimensions of competitive advantages (quality, cost, reliability, innovation, and responsiveness) of commercial organizations, where cloud computing services have the most significant influence on quality followed by cost and responsiveness, respectively. However, cloud computing services do not significantly influence innovation and reliability. Finally, the study recommends doing comparable research on other sectors, and industries as well as in other countries to test the results' generalizability.
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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 | 0.004 |
| 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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".