Differentiation Strategies of Internet Retailing (Unique, Value and Return): A Focused Web Evaluation into Airline Service Provider
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). Karl Cluck of Razorfish, recommends that “online marketers must enhance the user’s online experience in order to entice potential customers to buy” (“New York E-Commerce,” 2000, p. 1). A company can differentiate itself by creating a unique customer experience such as superior customer service and in turn, brand the experience. Through experience branding, ‘firms can greatly improve their ability to retain customers, target key customers segments and enhance network profitability’ (Vincent, 2000, p.25). The internet interactivity allows companies to respond more quickly to customer requests. Moreover, the ever-increasing speed of the Internet allows companies to communicate more quickly with current and potential customers, which is essential to retaining current customers and attracting new ones. The main purpose of the case study is to review and evaluate AirAsia’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 AirAsia 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 AirAsia CRM.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".