The favela effect: Spatial inequalities and firm strategies in disadvantaged urban communities
Bibliographic record
Abstract
Abstract Research summary E‐commerce firms make fewer products available and charge higher delivery prices to customers inside Brazilian favelas than they do to customers immediately outside favelas, despite the absence of infrastructure impediments at the favela borders. This phenomenological study uses mixed methods to investigate firm heterogeneity in these practices. The analysis shows that some firms treat favela consumers more equitably than their competitors. These firms (i) invest in physical stores inside and outside favelas, which are complementary to their online marketplaces, and (ii) engage genuinely with employees and consumers, which reflects their stakeholder orientation. By examining how firms operate in disadvantaged communities, scholars can enrich core theoretical constructs in strategic management, particularly by integrating insights from the fields of critical geography and urban economics. Managerial summary This study investigates whether firms operate differently in disadvantaged communities compared to co‐located nondisadvantaged areas. Findings show that operations in disadvantaged communities, such as favelas (Brazilian urban slums), demand specific investments that support transactions and contribute to realizing the underdeveloped potential of those communities. Firms succeed in commercial endeavors within disadvantaged communities by redeploying their resources and cultivating a stakeholder culture concomitantly. This strategy enables superior performance and the change‐making of structural inequalities to help alleviate poverty and develop urban communities.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".