Tourist shopping villages: exploring success and failure.
Bibliographic record
Abstract
This chapter explores the phenomenon of tourist shopping villages (TSVs) and the dimensions that contribute to their success by combining a traditional literature review with an expert knowledge mapping exercise. While shopping is seldom mentioned as a primary reason for travel, the activity is perhaps the most universal for tourists, and of great economic importance to local merchants. Creating comfortable and exciting shopping districts can entice tourists to visit and to extend their stay in the region. Many places around the world have developed into well-known tourist shopping destinations, whether by default or through deliberate planning. While tourist shopping can take many forms, this chapter is concerned with small tourist villages that base their appeal on retailing. TSVs are a growing phenomenon in many destinations and can be an important tool for regional development. The chapter draws on the work of Jansen-Verbeke (2000) and Getz (1994, 2000, 2006) to develop an initial framework for the systematic analysis of tourist shopping villages. The chapter includes an evaluation of 29 villages in Australia, New Zealand and Canada to explore factors relating to their perceived success. Onsite visits, rich photographic resources and the associated promotional materials offer a close inspection of the physical conditions of the settings, the activities available and the shopping styles and diversity. From this perspective, the perceived success of a tourist shopping village is strongly influenced by a well-developed heritage theme combined with the presentation of the village as larger in scale, tourist focused and tightly integrated. A successful village also is supported by regional distinctiveness in merchandise as well as regional food and wine. Accessibility and seasonality appear to have a minor influence on the success of shopping villages.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".