The Extent of Applying Value Chain Analysis to Achieve and Sustain Competitive Advantage in Jordanian Manufacturing Companies
Bibliographic record
Abstract
This study aims at identifying the extent of applying value chain analysis (VCA) to achieve and sustain competitive advantage in manufacturing companies in Jordan. To achieve the study’s objectives, a questionnaire was developed and pre -tested. The population of the study consists of the (93) company which are listed in the Amman Stock Exchange of Jordan in the end of the 2012, (65) companies of them accepted to fill the questionnaires. (81.5)% of the distributed questionnaires was received. Descriptive and analytical statistical techniques such as frequencies, percentages, standard deviation, means, one sample T test and one way ANOVA were applied to test the study’s hypotheses. The study revealed the following results: manufacturing companies in Jordan apply VCA, but don’t use it to achieve and sustain competitive advantage and there is no statistically significant effect of the respondents’ demographic characteristics on their perceiving the importance of applying VCA to achieve competitive advantage. The study recommends manufacturing companies in Jordan train their employees on strategic analysis of the company's internal and external environment, exercise the value chain analysis, calculate the unit cost of production and enter them in courses for achieving and sustaining competitive advantage through cost reduction and differentiation strategies.
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 | 0.005 | 0.009 |
| 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 | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".