Critical design characteristics for online retail stores in India
Bibliographic record
Abstract
Subject area E-commerce. Study level/applicability The case study is specific to the marketing demographics of online Indian shoppers and therefore, the inter-relationship between certain customer requirements and design elements and the relative importance of items in the latter may not follow the same pattern elsewhere. Case overview At a time when e-commerce is booming in India and when online retailers are posting multifold year-on-year growth, it becomes increasingly important to identify the factors pertaining to online stores which can influence the buying behavior of consumers. This case aims to explore such factors relevant to businesses as well as consumers so as to enable the next generation of leaders in online retail business to gain maximally. It deals with critical design characteristics of online retail stores in India which can prove crucial to their success. These characteristics are manifestations of various customer requirements. Two surveys are conducted to establish a hierarchy of design elements and quantify the inter-relationships between customer requirements and design characteristics. This is followed by leads as to which factors may or may not have contributed toward the declining sales volume of an e-commerce start-up, namely, E-Bazaar. Expected learning outcomes The learning objectives of the case include: the study of design characteristics with respect to their relative importance; the analysis of the degree of relationships between the design characteristics and customer requirements; and the interpretation of real-life signs in taking strategic business decisions in the field of e-commerce. The case aims to prepare a new breed of leaders in the e-commerce sector with a good level of relevant business acumen to help them make informed strategic choices. Supplementary materials Teaching Notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".