Putting a number on place: a systematic review of place branding influence
Bibliographic record
Abstract
Purpose The purpose of this paper is to systematically review and evaluate critically what is known about the attempts made to quantify the influence of place branding from a geographic perspective. In particular, this study reviews how scholars have conceptualized and measured place branding influence and provides directions for future research. Design/methodology/approach Through a systematic review of seven databases using an a priori defined search string, 39 articles attempting to quantify place branding influence were identified. These studies were reviewed and the paper information was used to explore how place branding research has thus far quantified branding’s influence. Findings There is a clear compatibility between place branding and human geography research domains, with a potential for place branding influence to be conceptualized through the sense-of-place, which has implications for place equity and consumer decision-making. Much of the existing studies have conceptualized influence through place equity, revealing potential performance indicators for its quantification. Research limitations/implications This study is based on research papers that attempt to quantify the effectiveness of place branding of urban areas. Limitations include the exclusion of qualitative studies which may provide alternative approaches to determine place branding outcomes. Originality value As a systematic review, the main contribution of this paper is a contemporary overview of how place branding influence has been quantified. It also provides valuable insights into the policy formulation and its implementation.
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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 | 0.017 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".