The impact of electronic commerce onorganizational structure: a case study of ecommercedecentralization
Bibliographic record
Abstract
This paper focuses on the problem of centralizing vs. decentralizing an organizational structure for e-commerce. First, a conceptual framework is designed based on the literature. Then a case study of the Brazilian subsidiary of a major chemical multinational is explained and analyzed. A decision-making method is applied to (a) identify the alternatives for organizational structures and evaluation criteria, and (b) determine which criteria enable one to identify an alternative as better than the others. In the context of the case, a centralized e-commerce structure was recommended. This paper makes two main contributions to theory. First, it shows the usefulness of the literature on R&D and innovation management as theoretical support for studies in other fields, in this case ecommerce organization. Second, it provides a methodology, which can be adapted for use by companies facing the same decision problem. Thoughts on possible future studies close the article.
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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".