Post-Pandemic Retail Design: Human Relationships with Nature and Customer Loyalty—A Case of the Grand Bazaar Tehran
Bibliographic record
Abstract
This article examines how human relationships with nature in the design of the Tehran Grand Bazaar can impact customer loyalty, and how this impact has been affected by the recent pandemic. As one of the most popular retail settings of the ancient Silk Road, the Grand Bazaar has a long history of micro-scale retailing and customer loyalty. This article reviews international guidelines of sustainable design using content analysis, identifying the most frequent guidelines related to human relationships with nature. It then defines customer loyalty in terms of various important non-financial measures of micro-scale retailing. The present article describes the development and collection of a structured survey conducted before the pandemic (March 2019), during the pandemic (March 2021), and after the pandemic (March 2023). The analysis shows a moderate to high relationship between sustainable design elements of the Bazaar (in terms of human relationships with nature) and customer loyalty before the COVID-19 pandemic. While this relationship fell to a moderate level in the middle of the pandemic, it rose dramatically to 89% by the end of the third year when governments eased public health and safety protocols. The results reveal that by adapting sustainable strategies that enhance human relationships with nature, designers and stakeholders can create post-pandemic retail settings that generate high customer loyalty.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".