E-Commerce: Compared Efficiency of Major Retailers in Brazil and Canada
Bibliographic record
Abstract
Retail e-commerce (B2C - business-to-customer) grows more in developing markets than in developed ones, however, most studies in the area were aimed at developed markets. This thesis aimed to compare the relative efficiency of publicly traded companies that operate in the retail of physical products online in a developed market (Canada) and another in development (Brazil), separately, and to discuss the practices of each market. To this end, a new instrumental approach was proposed, based on an integrated model of Data Envelopment Analysis (DEA) and Optimal Control Theory (OCT) to measure the efficiency of total inventory costs, based on the theory lean management, integrating the concept of capacity efficiency (i.e., management of physical assets). Also, the model directly incorporated inflation and, indirectly, the gross margin. The model was applied to public data from two sets of companies classified as Retail (Var) or Non-Durable Clothing and Consumer Goods (VBCnD) - one composed of 12 Canadian companies (CA) and another by 17 Brazilian companies (BR) -, during the period from 2016 to 2019. Then, a qualitative analysis of the B2C of the companies was made. The results showed that, in both countries, Var companies were more efficient than VBCnD. There are indications that BR companies were at a more advanced stage of the digital transition. In Brazil, Var companies prioritized sales by app and marketplace (own or third parties) and VBCnD companies, by app and virtual store. In Canada, Var companies have prioritized virtual stores and apps, while VBCnD, marketplaces (third parties), and apps. Although it is a common practice for BR Var companies, no observed CA company controls a marketplace. In Brazil, there was a strategy in which two companies operated in parallel, one specialized in B2C and the other in physical retail, though they were not more efficient than the market. The results pointed to BR companies Magazine Luiza, Arezzo, Estrela, and Via Varejo as market benchmarks, so their best practices should be studied.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".