A Mixed Method Approach to Evaluating eCommerce Website: Towards Socially-sensitive Guidelines for Future Design
Bibliographic record
Abstract
The goal of every business website, blog and online shop is increase traffic or customer engagement with a view to improve sales. Attractive user interfaces (UI) and user experiences (UX) are essential to achieving these marketing goals. Therefore, this paper uses a mixed-method approach to evaluate a foremost customer-centric eCommerce website in Africa to uncover the usability techniques employed in designing its user-interface and how they were implemented to provide positive UX to shoppers. As a first step towards contributing to research, we conducted an expert evaluation of Jumia, using Nielsen's Heuristics to identify usability problems on its user interface. Secondly, we conducted a user-study of 78 participants to understand the effects of usability techniques on customer's activities. Findings uncover that although Jumia's website is engaging and easy-to-use, however, there are usability issues which could affect its conversion rate. In addition, we attempt to rethink UI/UX designs of such platforms with a view to offering socially-relevant guidelines and recommendations for designing or redesigning such platforms based on the unique requirements of an average customer in the Global South. The findings from our study could guide all stakeholders in the user-centered design and indeed, eCommerce loop such managers, platform and brand developers, and marketers in designing new or improving existing eCommerce websites that target customers in the Global South.
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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 | 0.236 | 0.199 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".