EXPLORING LOCATION DETERMINANTS OF ASIA’S UNIQUE BEVERAGE SHOPS BASED ON A HYBRID MADM MODEL
Bibliographic record
Abstract
Identifying relevant location determinants is a good starting point for shop operators, help to increase profitability and, thus, avoiding business failure. Traditional Analytic Hierarchy Process (AHP) or the Analytic Network Process (ANP) have shortages that require improvement. Herein, Decision-Making Trial and Evaluation Laboratory (DEMATEL), ANP based on DEMATEL (DANP), and modified Vlse Kriterijumska Optimizacija I Kompromisno Resenje (modified VIKOR) are used to construct a hybrid multiple-attribute decision making (MADM) model, encompassing three dimensions and thirteen criteria in exploring the location determinants of Asia’s unique Bubble Tea Shops (BTSs) and to evaluate three preselected alternatives in Nanjing, China. The empirical findings of the DEMATEL method reveal that traffic traits (D1) and site traits (D2) are critical to BTSs, and that once these are enhanced, shop traits (D3) are also improved. Criteria deemed as important, based on the DEMATEL and DANP methodology, are (in descending order): proximity to a street corner (C2), proximity to public transportation systems (C1), road width (C3), proximity to communities (C5), proximity to commercial areas (C6), types of shop (C9), and proximity to schools (C7). Different decision-making rankings among alternatives are indicated based upon the modified VIKOR method and corresponding strategies for improvement are presented.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".