Cost - Driving Strategy Under the Use of E-Business to Achieve a Competitive Advantage in a Digital Economy Environment
Bibliographic record
Abstract
The study has aimed at identifying the developments the modern business environment, i.e e-business, witnessed; it also identify to which extent accounting information systems contribute to the achievement of competitive advantage of economic advantage through cost leadership strategy, which represents one of strategies of achieving competitive advantage. This strategy is significant and links to accounting work in digital economy environment and shifting toward e-application in business environment. The study has illustrated a set of fields which can be used in e-business environment in order to reduce the costs of economic units’ products in digital economy environment; industrial, marketing, administrative and finance cost may be reduced. In addition, the study has explained a set of accounting methods contributing to the achievement of cost strategy’s goals and competitive advantage of economic unit. The most important methods are as follows: activity-based cost system (ABC), value chain (VC), product life cycle (PLC), just on time product cost system (JIT) and target cost (TC). Research study has found that working in e-business environment have various advantages which can contribute to the reduction of product costs. It has also concluded that IT environment required by e-business can be used, thereby shifting towards e-accounting. Accordingly, designing a central database containing the data of applying cost management strategies is a necessity; this database also provides positive relationships among all accounting methods, thereby achieving the general goal, i.e the achievement of competitive advantage of economic unit through the reduction of product costs; product prices can be reduced at the markets.
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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 | 0.001 | 0.003 |
| 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 | 0.008 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".