Consumer Expectation from Online Retailers in Developing E-commerce Market: An Investigation of Generation Y in Bangladesh
Bibliographic record
Abstract
This paper aims to investigate the expectation from online retailers in the context of Generation Y, as the target market group in Bangladesh. It also examines the similarities and dissimilarities between the global online retail market trend and the target group. Volunteer final year university students conducted face to face survey using structured questionnaires. Primary data was collected using paper-and-pencil method where interviewee completed the form in most of the cases. This research found differences between the expectations of consumers from online retailers and the online market trend in developed countries. Existing issues and directions for future online businesses have been discussed. Most of the males want to purchase ‘Clothing and footwear’ online, whereas ‘Jewelleries and Watches’ is the most desired online product category by females. Two third of Gen Y in Bangladesh is already shopping online with high interest in F-commerce. Online shopping abandon rate is high due to service quality issue. Lowering internet cost is driving the e-commerce growth. 450 Gen Y respondents living in the capital have been surveyed, where internet service is optimum. Larger population from different age groups with different backgrounds, living in different cities can be examined in future. Need for reliable delivery service to support e-commerce growth, cash on delivery options, addition of Skype and Viber in customer service and success stories of Facebook stores have been revealed for the consideration of the existing and future market entrants. The findings should work as a guide for the international investors and MNCs. This study is the first of its kind in the context of Bangladesh and sets a benchmark. This research opens the door for continuous evaluation of the market and to venture new business and services as per consumer expectations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".