Seven Unique Differentiation Strategies toOnline Businesses:A Comprehensive Review of Malaysia Airline System (MAS)
Bibliographic record
Abstract
Differentiation is defined as the process of adding a set of meaningful and valued differences to distinguish the company’s offering from competitors’ offerings (Kotler, 2003, p. 315). The created value as obtain from being difference is to enhance the standard, performance review and establishment of one’s company. Therefore it’s important to strategize company’s differences in boosting up the profit, achievement and acknowledgement. The main purpose of the case study is to review and evaluate MAS’s website by applying Seven Unique Differentiation Strategies to Online Businesses (site environment/ atmospherics, making the intangible tangible, building trust, efficiency and timely order processing, pricing, CRM and enhancing the experience). In this study, qualitative data from MAS’s website was analyzed and discussed through proposed concise list of Seven Unique Differentiation Strategies to Online Businesses by Strauss and Frost, 2006. The study is expected to improve the differentiation of organization’s image and service information availability and accessibility on the Web in future. Finally, Researches agree to look into further the changes that should be made to enhance the Air Asia website evaluations and that changes are pertaining to virtual tours, appealing the 3-D images, immediate customer response and better “On Time Acknowledgement” for MAS’s CRM.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".